CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets

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Patil" }, { "@type": "Person", "name": "Sneha M. Pinto" }, { "@type": "Person", "name": "T. S. Keshava Prasad" }, { "@type": "Person", "name": "Prashant Kumar Modi" }, { "@type": "Person", "name": "Harsha Gowda" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": "Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/9-344/v2", "name": "CusVarDB: A tool for building customized sample-specific variant protein..." } } ] } Home Browse CusVarDB: A tool for building customized sample-specific variant protein... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Kasaragod S, Mohanty V, Tyagi A et al. CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.12688/f1000research.23214.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article Revised CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] Sandeep Kasaragod https://orcid.org/0000-0001-9499-3653 1 , Varshasnata Mohanty 1 , Ankur Tyagi 1 , [...] Santosh Kumar Behera 1 , Arun H. Patil 1 , Sneha M. Pinto 1 , T. S. Keshava Prasad 1 , Prashant Kumar Modi 1 , Harsha Gowda 1,2 Sandeep Kasaragod https://orcid.org/0000-0001-9499-3653 1 , Varshasnata Mohanty 1 , [...] Ankur Tyagi 1 , Santosh Kumar Behera 1 , Arun H. Patil 1 , Sneha M. Pinto 1 , T. S. Keshava Prasad 1 , Prashant Kumar Modi 1 , Harsha Gowda 1,2 PUBLISHED 16 Nov 2020 Author details Author details 1 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India 2 Institute of Bioinformatics, Bangalore, International Technology Park, Bangalore, 560066, India Sandeep Kasaragod Roles: Conceptualization, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Varshasnata Mohanty Roles: Methodology, Validation, Writing – Review & Editing Ankur Tyagi Roles: Methodology, Validation, Writing – Review & Editing Santosh Kumar Behera Roles: Conceptualization, Project Administration, Software, Writing – Review & Editing Arun H. Patil Roles: Project Administration, Software, Writing – Review & Editing Sneha M. Pinto Roles: Supervision, Writing – Review & Editing T. S. Keshava Prasad Roles: Conceptualization, Supervision, Writing – Review & Editing Prashant Kumar Modi Roles: Conceptualization, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Harsha Gowda Roles: Conceptualization, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Oncology gateway. Abstract Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively. READ ALL READ LESS Keywords Next-generation sequencing, Variant protein database, NGS-pipeline Corresponding Author(s) Prashant Kumar Modi ( [email protected] ) Harsha Gowda ( [email protected] ) Close Corresponding authors: Prashant Kumar Modi, Harsha Gowda Competing interests: No competing interests were disclosed. Grant information: SK is a recipient of the Indian Council of Medical Research (ICMR) Senior Research Fellow (SRF) application number [ISRM/11(27)/2017]. VM is a recipient of Women Scientist-A award from the Department of Science and Technology (DST). SKB is a recipient of DBT-BINC Junior Research Fellow. SMP is a recipient of INSPIRE Faculty Award from the Department of Science and Technology (DST), Government of India. We thank Karnataka Biotechnology and Information Technology Services (KBITS), Government of Karnataka, for the support to the Center for Systems Biology and Molecular Medicine at Yenepoya (Deemed to be University) under the Biotechnology Skill Enhancement Programme in Multiomics Technology (BiSEP GO ITD 02 MDA 2017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2020 Kasaragod S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Kasaragod S, Mohanty V, Tyagi A et al. CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.12688/f1000research.23214.2 ) First published: 11 May 2020, 9 :344 ( https://doi.org/10.12688/f1000research.23214.1 ) Latest published: 16 Nov 2020, 9 :344 ( https://doi.org/10.12688/f1000research.23214.2 ) Revised Amendments from Version 1 The new modifications for the articles are as follows 1. We removed some sentences in articles to avoid exceeding the word limit 2. As per the reviewer 1 and 2 comments, we modified the contents in the Introduction and Use case section 3. Supplementary Table 3 has been added to address the reviewer-2 comments 4. References 26-30 has been added The new modifications for the articles are as follows 1. We removed some sentences in articles to avoid exceeding the word limit 2. As per the reviewer 1 and 2 comments, we modified the contents in the Introduction and Use case section 3. Supplementary Table 3 has been added to address the reviewer-2 comments 4. References 26-30 has been added To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table. READ REVIEWER RESPONSES Introduction Cancer genome sequencing projects have revealed thousands of genomic variations in cancers ( Forbes et al ., 2010 ; Tate et al ., 2019 ; Tomczak et al., 2015 ; Zhang et al., 2011 ). Some mutants are oncogenic and drive proliferation in various cancers. For example, amino acid substitution of arginine to leucine at position 858 (L858R) in the epidermal growth factor receptor (EGFR) gene has been observed in 17% of pulmonary adenocarcinoma patients ( Morgensztern et al ., 2015 ). In addition, a mutation in gene BRAF V600E is known to drive some melanomas ( Chapman et al ., 2011 ). Some of these mutant proteins are proteolytically processed in cancer cells, resulting in major histocompatibility complex (MHC) presentation of mutant peptides. These mutant peptides serve as neo-antigens that recruit T cells and result in immune activation ( Kreiter et al ., 2015 ). However, not all mutants are encoded at the protein level. Therefore, it is important to identify mutant proteins expressed by cancer cells. However, there are no easy-to-use pipelines for biologists to identify such coding variants, which alter the protein sequences and may play an important role in tumorigenesis. Detection of cancer-specific proteoforms has been studied by several research teams using proteogenomics methods. This approach integrates proteomics data with genome sequence data to identify protein complement of genomic variants ( Menschaert & Fenyö, 2017 ; Nesvizhskii, 2014 ; Ruggles & Fenyö, 2016 ). Detection of coding variants can provide candidate molecules for novel therapeutic interventions ( Subbannayya et al ., 2016 ). Several tools have been developed in the last decade to carry out onco-proteogenomics. CPTAC ( Ellis et al ., 2013 ) program was initiated to understand the complexity of cancer and its sub types using multi omics approach. Various tools and approaches have been employed to identify mutant peptides in cancers ( Mathivanan et al ., 2012 ) ( Yeom et al ., 2016 ) ( Nagaraj et al ., 2015 ). Several qualitative and quantitative proteomics studies have been reported, which identify altered expression of proteins in cancers ( Subbannayya et al ., 2015 ). Often, conventional workflows were used in such investigations, wherein a reference protein database was used to search tandem mass spectrometry data for identification and quantification of proteins ( Kelkar et al., 2014 ). However, such a reference database is usually devoid of sample-specific amino acid variations resulting from genomic alterations. The publicly available databases such as dbSNP ( Sherry et al ., 2001 ), COSMIC ( Forbes et al ., 2015 ) and UniProt ( Apweiler et al ., 2004 ) can be used to identify the variant peptides ( Alfaro et al ., 2017 ) but millions of protein-variants from these databases might increase the probability of identifying false positives. Therefore, there is a need for an improved method to identify sample specific sequence variations at the proteomic level. Tools such as CustomProDB ( Wang & Zhang, 2013 ) and MZVar ( https://bitbucket.org/sib-pig/mzvar-public/src/master/ ) have been developed for generating variant protein database. These tools require processed files such as VCF or BED file as an input. Executing the preprocessing steps requires knowledge of computation and also requires multiple tools to generate the output. Hence, we developed CusVarDB with an in-built pipeline for genomics suite to identify variants and create custom variant protein database. Methods Implementation CusVarDB is available at https://sourceforge.net/projects/cusvardb/ and http://bioinfo-tools.com/Downloads/CusVarDB/V1.0.0/ . Our tool supports graphical user interface (GUI) for easy execution of next-generation sequencing (NGS) pipelines. The GUI was developed using Microsoft Visual Studio Community edition 2017. Linux commands were executed through the Python program (terminal.py) developed by Linwei available on GitHub . Portable version of Perl was used to execute the Annovar scripts. A python script was used to generate the custom variant database; the scripts were made portable using PyInstaller . The dry-run concept is implemented in the tool to customize commands according to user’s need and run in batches. CusVarDB inherits different NGS pipelines for genomics, RNA-Seq and exome-seq datasets. The tool performs the following steps: i) alignment of genomic data to a reference genome; ii) variant calling; iii) variant annotation; and iv) generate variant protein database ( Figure 1 ). Burrows-Wheeler Aligner (BWA) is executed for alignment of genome and exome ( Li & Durbin, 2009 ) data, while HISAT2 is used for RNA-Seq data ( Kim et al ., 2015 ). Subsequent steps involving sample labelling, variant calling and annotation are performed using Picard , GATK ( McKenna et al ., 2010 ) and ANNOVAR ( Wang et al ., 2010 ), respectively. CusVarDB substitutes the amino acid variations from the previous steps to generate a custom variant protein database. Figure 1. Schematic representation of the workflow. Operation CusVarDB runs on Windows 10 system. It requires Linux Bash Shell and ANNOVAR tool to be enabled and installed by the user. Minimum system requirements include Intel i5 or i7, having at least four cores with 8 GB of RAM and 1 TB hard drive. High performance processors such as Intel i9 or Xeon and large quantity of RAM can enable faster execution of tasks. The tool requires FASTA, FASTQ and dbSNP database as input files. FASTA file will be loaded at configuration panel and indexed using bwa index or hisat2-build command. The quality control panel loads the FASTQ file and produces a quality check report using the FastQC tool. The third panel asks the user to load the FASTQ file(s), set the number of threads and RAM to perform the alignment and variant calling steps. This step produces the raw VCF file, which will be used for annotation in annotation tab. The annotation panel annotates the variants and incorporates those variants to the protein database to create a custom variant protein database. Detailed information of tool installation and execution of a test dataset are available in the manual (Manual.pdf) available to download here and on Zenodo ( Kasaragod et al ., 2020b ). Use cases methods The genomics datasets downloaded from the SRA repository were subjected to a quality check using FastQC tool. Poor quality reads with Phred score less than twenty were trimmed using fastx_trimmer. Trimmed reads were mapped to human reference genome version 19 (hg19) . AddOrReplaceReadGroup and Markduplicates operations from GATK were performed to add label information and to remove polymerase chain reaction (PCR) artefacts. Further, post alignment processing steps such as IndelRealigner and BaseRecalibrator were performed to correct the mapping errors made by alignment tool to increase variant calling accuracy. Variant calling was performed using HaplotypeCaller. Finally, raw variants were annotated using ANNOVAR and the annotated variants were incorporated to the protein database to create custom variant protein database. Proteomic searches were carried out using Proteome Discoverer 2.3 (Thermo Scientific, Bremen, Germany). Searches could also be carried out using freely available open source alternatives such as MaxQuant , MsFragger , MS-GF+ , MyriMatch or OMSSA . Mass spectrometry raw files were obtained from the PRIDE archive ( PXD008222 ) and searched against the customized variant protein database using SEQUEST-HT search algorithm. Trypsin and LysC were set as proteolytic enzymes with a maximum missed cleavage of one. Carbamidomethylation of cysteine was set as a fixed modification, and acetylation of protein N-terminus and oxidation of methionine were set as variable modifications with a minimum peptide length of seven amino acids. The precursor mass tolerance was fixed as 10 ppm, and 0.05 Da for fragment ions. Mass spectrometry data were searched against the decoy database with 1% false discovery rate cut-off at the peptide level. We have provided a dataset for users to test the tool. The test dataset was taken from the study SRR7418758 archived on the NCBI Sequence Read Repository (SRA) and aligned using human reference genome version 19 (hg19) . Using samtools view command, reads mapped to chromosome 22 were extracted and converted to FASTQ files (paired-end). We have also provided chromosome 22 nucleotide sequences from hg19 and corresponding variant information from dbSNP database . Use cases As a test case, we analyzed exome ( Daemen et al ., 2013 ) and proteome datasets ( Lawrence et al., 2015 ) from breast cancer cell lines. We incorporated 12,429; 13,923; 12,386 and 11,600 non-synonymous SNPs from BT474 (accession number SRR925752 ), MDMAB157 (accession number SRR925788 ), MFM223 (accession number SRR925796 ) and HCC38 (accession number SRR925778 ) cells, respectively, to the protein database ( Figure 2A ). These non-synonymous SNPs were incorporated to the reference protein database (Human RefSeq release 93) to create customized variant protein database. Mass spectrometry-based raw files were searched against their respective custom variant protein database, which resulted in identification of 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38, cell line datasets ( Figure 2B ). Interestingly, we observed mutant protein expression of Replication Timing Regulatory Factor 1 (RIF1) and Torsin-1A-interacting protein 1 (TOR1AIP1) across all four breast cancer cell lines. Mutant plectin (PLEC), marker Of Proliferation Ki-67 (MKI67), HEAT Repeat Containing 1 (HEATR1) and AHNAK nucleoprotein (AHNAK) were detected in three of the four breast cancer cell lines. These coding mutations have also been reported in other cancers The resultant variant peptide lists are available as Underlying data ( Kasaragod et al. , 2020a ). The data generated from the tool demonstrates the usefulness and the ease of detection of variant peptides in an integrated omics analysis. Figure 2. A ) Number of nonsynonymous variants incorporated to protein database. B ) Variant peptides identified across four cell lines (BT474, MDMAB157, MFM223 and HCC38) by searching mass spectrometry data against sample specific protein database. Conclusions CusVarDB generates customized variant protein database from NGS datasets. To our knowledge, this is the first tool which uses Linux Bash Shell to execute the NGS tools on a Windows OS. The tool provides additional feature of dry-run, making the commands highly customizable. The variant protein database generated by this tool is highly compatible for use as a reference protein database for analysis of variants at the proteomic level. CusVarDB currently allows incorporation of non-synonymous mutations. It does not allow incorporation of protein variations resulting from indels and frame-shifts. We developed a flexible tool with the intension of updating it in the future. Data availability Source data Whole exome data from Daemen et al. (2013) on BioProject, Accession number PRJNA210427 Mass spectrometry proteome data from Lawrence et al. (2015) on PRIDE, Accession number PXD008222: https://identifiers.org/pride.project:PXD008222 Test dataset on SRA, Accession number SRR7418758: https://identifiers.org/insdc.sra:SRR7418758 Underlying data Zenodo: CusVarDB: A tool for building customized sample-specific variant protein database from Next-generation sequencing datasets. http://doi.org/10.5281/zenodo.4018694 ( Kasaragod et al. , 2020a ) This project contains the following underlying data: - Gowda_CusVarDB_Supplementary_table1.xlsx (This table contains the resultant variant peptides along with the wild-type peptides from BT474, MDMAB157, MFM223, and HCC38 datasets. Along with mutant peptides, this section also provides additional information such as peptide-spectrum match [PSM], Protein accession, cross-correlation value from the search [Xcorr] and retention time [RT]) - Gowda_CusVarDB_Supplementary_table2.xlsx (This table provides the complete details of the resultant peptides. Here the mutant and corresponding wild-type peptides are mentioned in different sheets. For a given mutant peptide its wild-type peptide and corresponding information can be mapped using the VLOOKUP function in Excel by keeping column A [Sl.No] as lookup parameter.) - Gowda_CusVarDB_Supplementary_table3.xlsx (This table briefs about the variants which are already reported in other cancers.) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Software available from: https://sourceforge.net/projects/cusvardb/ and http://bioinfo-tools.com/Downloads/CusVarDB/V1.0.0/ Source code available from: https://github.com/sandeepkasaragod/CusVarDB Archived source code at time of publication: https://doi.org/10.5281/zenodo.3780645 ( Kasaragod et al. , 2020b ) License: Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements We thank Mr. Hitesh Ugaram Kore, Ph.D. student, Cancer Precision Medicine Group, QIMR Berghofer Medical Research Institute, Australia for his assistance with testing the tool and fixing the bugs which has greatly improved the performance of the tool. Faculty Opinions recommended References Alfaro JA, Ignatchenko A, Ignatchenko V, et al. : Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines. Genome Med. 2017; 9 (1): 62. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 11 May 2020 ADD YOUR COMMENT Comment Author details Author details 1 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India 2 Institute of Bioinformatics, Bangalore, International Technology Park, Bangalore, 560066, India Sandeep Kasaragod Roles: Conceptualization, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Varshasnata Mohanty Roles: Methodology, Validation, Writing – Review & Editing Ankur Tyagi Roles: Methodology, Validation, Writing – Review & Editing Santosh Kumar Behera Roles: Conceptualization, Project Administration, Software, Writing – Review & Editing Arun H. Patil Roles: Project Administration, Software, Writing – Review & Editing Sneha M. Pinto Roles: Supervision, Writing – Review & Editing T. S. Keshava Prasad Roles: Conceptualization, Supervision, Writing – Review & Editing Prashant Kumar Modi Roles: Conceptualization, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Harsha Gowda Roles: Conceptualization, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information SK is a recipient of the Indian Council of Medical Research (ICMR) Senior Research Fellow (SRF) application number [ISRM/11(27)/2017]. VM is a recipient of Women Scientist-A award from the Department of Science and Technology (DST). SKB is a recipient of DBT-BINC Junior Research Fellow. SMP is a recipient of INSPIRE Faculty Award from the Department of Science and Technology (DST), Government of India. We thank Karnataka Biotechnology and Information Technology Services (KBITS), Government of Karnataka, for the support to the Center for Systems Biology and Molecular Medicine at Yenepoya (Deemed to be University) under the Biotechnology Skill Enhancement Programme in Multiomics Technology (BiSEP GO ITD 02 MDA 2017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 16 Nov 2020, 9:344 https://doi.org/10.12688/f1000research.23214.2 version 1 Published: 11 May 2020, 9:344 https://doi.org/10.12688/f1000research.23214.1 Copyright © 2020 Kasaragod S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Kasaragod S, Mohanty V, Tyagi A et al. CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.12688/f1000research.23214.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 16 Nov 2020 Revised Views 0 Cite How to cite this report: Duitama J and Mahecha D. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74962 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74962 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Dec 2020 Jorge Duitama , Systems and Computing Department, Universidad de Los Andes, Bogotá, Colombia Daniel Mahecha , Systems and Computing Department, Universidad de Los Andes, Bogotá, Colombia Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.30424.r74962 This manuscript by Kasagarod et al. , describes a GUI-based tool that executes a pipeline of Linux based NGS tools on a Windows OS, to create a variant protein database specific for a sample. This variant protein database can be further ... Continue reading READ ALL This manuscript by Kasagarod et al. , describes a GUI-based tool that executes a pipeline of Linux based NGS tools on a Windows OS, to create a variant protein database specific for a sample. This variant protein database can be further used to identify mutant proteins on mass spectrometry data from proteomics studies. The manuscript claims that CusVarDB is a useful tool on the onco proteo-genomics and different from other tools that require BED or VCF files as input, while it works with FASTA and FASTQ files as an input. As a use case, it provides examples of its use in genomics and proteomics data from four cancer cell lines. Unfortunately, in my opinion there are several technical and conceptual issues related to this work: Regarding decisions on technological architecture, it is not clear which is the advantage of building an application on technology that only runs on windows to run programs that run mostly on Linux. As a consequence, I could not install and execute the software successfully. The manuscript claims that VCF files are difficult to generate because: “Executing the preprocessing steps requires knowledge of computation and also requires multiple tools to generate the output.”. However, there are different commercial and open source solutions to analyze sequencing reads and generate VCF files. As an example, my research group maintains the Next-Generation Sequencing Experience Platform , which is able to make this analysis with a graphical interface on any operative system. Other well known web based solutions are Galaxy or CyVerse . For variants interpretation, functionalities of CusVarDB should be compared with those offered by well known platforms such as VEP . One common issue with wrapping tools for other software such as GATK and bwa is that the software tools are evolving and hence the wrapping software quickly becomes outdated. Regarding this issue, the manuscript does not describe if a maintenance plan is planned for this tool, including the way different enclosed tools will be updated. In the section “use cases”, the manuscript describes one single use case in which alternative proteins are identified for four samples. It is not clear which parameters were used to run the mapping and variant calling tools, and especially to filter variants. It is not clear if the reported variants are the complete set of variants after this process. The false positive rate of the proposed analysis pipeline should be estimated and compared with other pipelines to identify variants in cancer samples. Moreover, it is not clear if tumor and normal tissue was considered to increase the reliability of the reported variants. If the section is called “use cases”, then more use cases should be included. From the description of the use case, it looks like both genomics and proteomics data is required to operate CusVarDB. Obtaining both data types can be too expensive for real clinical settings. The manuscript should clearly state if both data types are required or what can be done if only DNA sequencing data is available. The conclusions state that the variant protein database generated by this tool is highly compatible for use as a reference protein database for analysis of variants at the proteomic level. However, from the manuscript and the manual it is not clear what format the output variant protein database has and what information does it include, and whether the output format is compatible with commonly used proteomics tools. If the database is relational, an entity relationship model should be provided and instructions on how to install and use the database should be provided and maintained as part of the documentation. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: My research group maintains the open source software tool Next-Generation Sequencing Experience Platform, which currently has functionalities to analyze DNA sequencing reads and generate VCF files. NGSEP also has a graphical interface and can run on any operative system. Reviewer Expertise: Bioinformatics, applied algorithms, software development, population genomics We confirm that we have read this submission and believe that we have an appropriate level of expertise to state that we do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Duitama J and Mahecha D. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74962 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74962 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Mathivanan S. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74866 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74866 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 23 Nov 2020 Suresh Mathivanan , Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Vic, Australia Approved VIEWS 0 https://doi.org/10.5256/f1000research.30424.r74866 All ... Continue reading READ ALL All issues addressed. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Cancer proteomics and bioinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mathivanan S. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74866 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74866 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Kandasamy RK. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74867 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74867 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Nov 2020 Richard Kumaran Kandasamy , Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway Approved VIEWS 0 https://doi.org/10.5256/f1000research.30424.r74867 The authors have addressed the minor issues ... Continue reading READ ALL The authors have addressed the minor issues and I recommend accepting the article for indexing. Competing Interests: No competing interests were disclosed. Reviewer Expertise: My research expertise include proteomics, proteogenomics, bioinformatics and systems immunology. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kandasamy RK. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74867 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74867 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 11 May 2020 Views 0 Cite How to cite this report: Kandasamy RK. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63338 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63338 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Aug 2020 Richard Kumaran Kandasamy , Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.25627.r63338 The manuscript by Kasaragod et al., describes a software application for developing sample-specific protein sequence database that takes into account the protein-coding variants, for interpretation of mass spectrometry data to see what portion of the variants are seen at the ... Continue reading READ ALL The manuscript by Kasaragod et al., describes a software application for developing sample-specific protein sequence database that takes into account the protein-coding variants, for interpretation of mass spectrometry data to see what portion of the variants are seen at the protein expression level. This is an important aspect in exemplifying the use of genomic data on proteomics datasets associated with cancer samples. The software is made available for download and enough documentation has been provided for potential users. The authors use this data set to identify protein-level evidence for expression of variants in four different cancel cell lines. The manuscript can be accepted for indexing. I have one important point that I would like the authors to address: While the findings are potentially interesting, it would be nice if the authors pick a few important examples specific to these four cell lines. This would give the users a better appreciation of the biological insights given by this application. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: My research expertise include proteomics, proteogenomics, bioinformatics and systems immunology. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kandasamy RK. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63338 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63338 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Mathivanan S. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63336 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63336 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 23 Jul 2020 Suresh Mathivanan , Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Vic, Australia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.25627.r63336 This is a manuscript that describes an important tool. The authors may need to discuss other proteogenomic tools similar to this and provide the advantages. If such tools don't exist, perhaps make a strong case. Zhang ... Continue reading READ ALL This is a manuscript that describes an important tool. The authors may need to discuss other proteogenomic tools similar to this and provide the advantages. If such tools don't exist, perhaps make a strong case. Zhang lab tools can also be discussed with references. Can the authors also discuss and refer previous attempts on onco-proteogenomics? One of the first paper on onco-proteogenomics came in 2012: Mathivanan, S., Ji, H., Tauro, B.J., Chen, Y.S. and Simpson, R.J. (2012) Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry. Journal of Proteomics . 76, 141-9 1 . Does CurVardb provide only non-synonymous point mutations? What about frame shit mutations? Is that also provided or perhaps will be added in next version. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Mathivanan S, Ji H, Tauro BJ, Chen YS, et al.: Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry. J Proteomics . 2012; 76 Spec No. : 141-9 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Cancer proteomics and bioinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mathivanan S. Reviewer Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63336 ) The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63336 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 11 May 2020 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 16 Nov 20 read read read Version 1 11 May 20 read read Suresh Mathivanan , La Trobe University, Melbourne, Australia Richard Kumaran Kandasamy , Norwegian University of Science and Technology, Trondheim, Norway Jorge Duitama , Universidad de Los Andes, Bogotá, Colombia Daniel Mahecha , Universidad de Los Andes, Bogotá, Colombia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Duitama J et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Dec 2020 | for Version 2 Jorge Duitama , Systems and Computing Department, Universidad de Los Andes, Bogotá, Colombia Daniel Mahecha , Systems and Computing Department, Universidad de Los Andes, Bogotá, Colombia 0 Views copyright © 2020 Duitama J et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript by Kasagarod et al. , describes a GUI-based tool that executes a pipeline of Linux based NGS tools on a Windows OS, to create a variant protein database specific for a sample. This variant protein database can be further used to identify mutant proteins on mass spectrometry data from proteomics studies. The manuscript claims that CusVarDB is a useful tool on the onco proteo-genomics and different from other tools that require BED or VCF files as input, while it works with FASTA and FASTQ files as an input. As a use case, it provides examples of its use in genomics and proteomics data from four cancer cell lines. Unfortunately, in my opinion there are several technical and conceptual issues related to this work: Regarding decisions on technological architecture, it is not clear which is the advantage of building an application on technology that only runs on windows to run programs that run mostly on Linux. As a consequence, I could not install and execute the software successfully. The manuscript claims that VCF files are difficult to generate because: “Executing the preprocessing steps requires knowledge of computation and also requires multiple tools to generate the output.”. However, there are different commercial and open source solutions to analyze sequencing reads and generate VCF files. As an example, my research group maintains the Next-Generation Sequencing Experience Platform , which is able to make this analysis with a graphical interface on any operative system. Other well known web based solutions are Galaxy or CyVerse . For variants interpretation, functionalities of CusVarDB should be compared with those offered by well known platforms such as VEP . One common issue with wrapping tools for other software such as GATK and bwa is that the software tools are evolving and hence the wrapping software quickly becomes outdated. Regarding this issue, the manuscript does not describe if a maintenance plan is planned for this tool, including the way different enclosed tools will be updated. In the section “use cases”, the manuscript describes one single use case in which alternative proteins are identified for four samples. It is not clear which parameters were used to run the mapping and variant calling tools, and especially to filter variants. It is not clear if the reported variants are the complete set of variants after this process. The false positive rate of the proposed analysis pipeline should be estimated and compared with other pipelines to identify variants in cancer samples. Moreover, it is not clear if tumor and normal tissue was considered to increase the reliability of the reported variants. If the section is called “use cases”, then more use cases should be included. From the description of the use case, it looks like both genomics and proteomics data is required to operate CusVarDB. Obtaining both data types can be too expensive for real clinical settings. The manuscript should clearly state if both data types are required or what can be done if only DNA sequencing data is available. The conclusions state that the variant protein database generated by this tool is highly compatible for use as a reference protein database for analysis of variants at the proteomic level. However, from the manuscript and the manual it is not clear what format the output variant protein database has and what information does it include, and whether the output format is compatible with commonly used proteomics tools. If the database is relational, an entity relationship model should be provided and instructions on how to install and use the database should be provided and maintained as part of the documentation. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests My research group maintains the open source software tool Next-Generation Sequencing Experience Platform, which currently has functionalities to analyze DNA sequencing reads and generate VCF files. NGSEP also has a graphical interface and can run on any operative system. Reviewer Expertise Bioinformatics, applied algorithms, software development, population genomics We confirm that we have read this submission and believe that we have an appropriate level of expertise to state that we do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Duitama J and Mahecha D. Peer Review Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74962) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74962 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Mathivanan S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 23 Nov 2020 | for Version 2 Suresh Mathivanan , Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Vic, Australia 0 Views copyright © 2020 Mathivanan S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions All issues addressed. Competing Interests No competing interests were disclosed. Reviewer Expertise Cancer proteomics and bioinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Mathivanan S. Peer Review Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74866) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74866 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Kandasamy R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Nov 2020 | for Version 2 Richard Kumaran Kandasamy , Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway 0 Views copyright © 2020 Kandasamy R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have addressed the minor issues and I recommend accepting the article for indexing. Competing Interests No competing interests were disclosed. Reviewer Expertise My research expertise include proteomics, proteogenomics, bioinformatics and systems immunology. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Kandasamy RK. Peer Review Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.30424.r74867) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-344/v2#referee-response-74867 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Kandasamy R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Aug 2020 | for Version 1 Richard Kumaran Kandasamy , Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway 0 Views copyright © 2020 Kandasamy R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript by Kasaragod et al., describes a software application for developing sample-specific protein sequence database that takes into account the protein-coding variants, for interpretation of mass spectrometry data to see what portion of the variants are seen at the protein expression level. This is an important aspect in exemplifying the use of genomic data on proteomics datasets associated with cancer samples. The software is made available for download and enough documentation has been provided for potential users. The authors use this data set to identify protein-level evidence for expression of variants in four different cancel cell lines. The manuscript can be accepted for indexing. I have one important point that I would like the authors to address: While the findings are potentially interesting, it would be nice if the authors pick a few important examples specific to these four cell lines. This would give the users a better appreciation of the biological insights given by this application. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise My research expertise include proteomics, proteogenomics, bioinformatics and systems immunology. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Kandasamy RK. Peer Review Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63338) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63338 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Mathivanan S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 23 Jul 2020 | for Version 1 Suresh Mathivanan , Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Vic, Australia 0 Views copyright © 2020 Mathivanan S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is a manuscript that describes an important tool. The authors may need to discuss other proteogenomic tools similar to this and provide the advantages. If such tools don't exist, perhaps make a strong case. Zhang lab tools can also be discussed with references. Can the authors also discuss and refer previous attempts on onco-proteogenomics? One of the first paper on onco-proteogenomics came in 2012: Mathivanan, S., Ji, H., Tauro, B.J., Chen, Y.S. and Simpson, R.J. (2012) Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry. Journal of Proteomics . 76, 141-9 1 . Does CurVardb provide only non-synonymous point mutations? What about frame shit mutations? Is that also provided or perhaps will be added in next version. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Mathivanan S, Ji H, Tauro BJ, Chen YS, et al.: Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry. J Proteomics . 2012; 76 Spec No. : 141-9 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Cancer proteomics and bioinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Mathivanan S. Peer Review Report For: CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets [version 2; peer review: 2 approved, 1 not approved] . F1000Research 2020, 9 :344 ( https://doi.org/10.5256/f1000research.25627.r63336) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-344/v1#referee-response-63336 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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