SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence

Peter T. Habib; Alsamman M. Alsamman; Sameh E. Hassanein; Ghada A. Shereif; Aladdin Hamwieh

doi:10.12688/f1000research.21556.1

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Software Tool Article

SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence

[version 1; peer review: 1 approved with reservations]

Peter T. Habib ¹, Alsamman M. Alsamman², Sameh E. Hassanein³, Ghada A. Shereif⁴, Aladdin Hamwieh¹

Peter T. Habib ¹, Alsamman M. Alsamman², [...] Sameh E. Hassanein³, Ghada A. Shereif⁴, Aladdin Hamwieh¹

PUBLISHED 20 Dec 2019

Author details Author details

¹ Department of Biodiversity and Crop Improvement, International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt
² Department of Genome Mapping, Molecular Genetics and Genome Mapping Laboratory, Agricultural Genetic Engineering Research Institute (AGERI), Giz, Egypt
³ Department of Bioinformatics & Computer Networks, October 6 University, Giza, Egypt
⁴ Faculty of Pharmacy, October 6 University, Giza, Egypt

Peter T. Habib
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Alsamman M. Alsamman
Roles: Investigation, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Sameh E. Hassanein
Roles: Supervision

Ghada A. Shereif
Roles: Validation, Writing – Review & Editing

Aladdin Hamwieh
Roles: Funding Acquisition, Supervision

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Python collection.

Abstract

Due to the ability to diagnose diseases early and evaluate the effectiveness of medicinal drugs, single nucleotide polymorphism (SNP) identification receives significant interest. Detection and diagnosis of genetic variation through skill-less computational tools would help researchers reducing the severity of such health complications and improving well-tailored therapies using discovered and previously known information. We introduce SNPector, which is a standalone SNP inspection software, which can be used to diagnose gene pathogenicity and drug reaction in naked genomic sequences. It identifies and extracts gene-related SNPs, and reports their genomic position, associated phenotype disorder, associated diseases, linkage disequilibrium, in addition to various drug reaction information. SNPector detects and verifies the existence of an SNP in a given DNA sequence based on different clinically relevant SNP databases, such as NCBI ClinVar, AWESOME, and PharmGKB, and generates highly informative visualizations of the recovered information.

Keywords

SNP, Disease, Python, Bioinformatics

Corresponding authors: Peter T. Habib, Aladdin Hamwieh

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2019 Habib PT 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: Habib PT, Alsamman AM, Hassanein SE et al. SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence [version 1; peer review: 1 approved with reservations]. F1000Research 2019, 8:2133 (https://doi.org/10.12688/f1000research.21556.1) First published: 20 Dec 2019, 8:2133 (https://doi.org/10.12688/f1000research.21556.1) Latest published: 20 Feb 2020, 8:2133 (https://doi.org/10.12688/f1000research.21556.2)

Introduction

In recent years, the number of cases of genetically originated diseases has increased, alarming the world and sparking interest in the development of precision medicine using molecular biomarkers. Single nucleotide polymorphisms (SNPs), the most common genetic difference among individuals, occurs in the human genome. These randomized modifications in DNA bases cause alterations in protein sequence residues of amino acids, thus altering their functions, which leads to different disease conditions in individuals¹. Several of these SNPs have been identified as disease-related genetic markers that have been used to recognize genes responsible for particular diseases in humans².

Distinguishing the evidence and the interpretation of a rich range of markers will be necessary to relate major alterations in the SNPs and to discover their connection in the progression of disease. Clarification of the phenotypic-associative mechanisms for these variations is therefore vital for comprehending the sub-atomic subtleties of disease origin and for developing novel therapeutic methods^3,4.

Although SNPs may exist in various areas of the gene, such as promoters, introns, 5′-and 3′ UTRs, to date, most research has focused on disease-associated SNPs in coding regions or exons, especially non-synonymous SNPs, which may alter the biochemical ability of encoded proteins. In turn, altering gene promoters impact gene expression by changing transcription, binding transcription factor, methylation of DNA and modifications of histones. As a consequence, changes in gene expression, their impact on disease susceptibility, and drug responses can differ depending on the location of the SNP^5–7.

With the expansion of genetic variants, different software could be used to generate new knowledge to support disease diagnosis and drug response studies and to develop new biomarkers for disease identification and drug customization. In this regard, a number of software applications have been developed in the last few years to classify, prioritize and evaluate the impact of genomic variants. For example, the Ensembl Variant Effect Predictor offers access to a large range of genomic annotations, with a variety of frameworks that answer different needs, with easy setup and evaluation methods⁸. Similarly, SnpEff categorizes the results of genome sequence variations, annotates variants according to their genomic location and estimates the coding effects. Depending on genome annotation, it is possible to predict coding effects such as non-synonymous or synonymous substitution of amino acids, stop codon gains or losses, start codon gains or losses, or frame changes⁹.

On the other hand, another tool, PolyPhen-2, assesses the potential impact of the genetic substitution of amino acids on the basis of physical, evolutionary comparative factors and model structural changes. Based on these profiles, the probability of a missense mutation becoming dangerous is measured on the basis of a combination of all these properties¹⁰. Similarly, SIFT calculates whether the substitution of amino acids affects protein activity, based on the homology of sequences and the physical properties of amino acids. It may be used for non-synonymous polymorphisms and laboratory-induced missense mutations that naturally occur, to effectively classify the effects of SNPs as well as other types, including multiple nucleotide polymorphisms¹¹.

Moreover, Phyre2 is a web-based suite of tools for predicting and analysing protein structure, function and mutations. It has sophisticated remote homology identification methods to build 3D models, anticipate ligand binding sites, and evaluate the effect of amino acid variants, e.g. non-synonymous SNPs¹². Missense 3D uses the user-provided UniProt ID of the query protein, wild-type residue and substitution and other information to generate PDB residue mapping and predict the substitution effect on the 3D protein structure¹³.

To conclude the effect and possible phenotype of SNP, these software and web applications require minimum information such as SNP genomic position, SNP ID, allele form, and/or gene name. Acquiring this information requires using different computational tools, extensive time and some analysis skills. Most of the time, only gene sequences are available in which the SNPs are hidden without any additional information.

We therefore introduce SNPector, a standalone SNP inspection software that can be used to diagnose gene pathogenicity and drug reaction in naked genomic sequences. SNPector identifies and extracts gene-related SNPs, and reports their genomic position, associated phenotype disorder, associated diseases, linkage disequilibrium, in addition to various drug reaction information. It detects and verifies the existence of an SNP in a given DNA sequence based on different clinically relevant SNP databases, such as NCBI ClinVar¹⁴, AWESOME¹⁵, and PharmGKB¹⁶. Lastly, it connects identified SNPs, related diseases and drugs, and produces numerous visualization figures to explain these relationships with the support of different Python modules.

Methods

Functions

The SNPector Python tool uses many packages to inspect the existence of SNPs in a given sequence. Moreover, SNPector provides users with detailed visualization figures, highlighting other SNPs with similar mutation effects on protein phosphorylation, ubiquitination, methylation, or sumoylation sites, and predicts substrates of N-acetyltransferase.

Additionally, SNPector provides the ability to visualize obtained information about the linkage disequilibrium of detected SNPs using various Python packages, such as Matplotlib¹⁷, generating a number of figures that summarize vast amounts of previously published data indicating SNPs allelic segregation, association, minor allele frequency. Figure 3 shows an example of illustrations that can be generated through SNPector.

Operation

SNPector requires at least Python 3.5, 16GB RAM, i7 cores, and 8 MB.

Workflow

SNPector was written using Pythpn3 programming language as a standalone package and can be run on different operating systems platforms supported with Python 3.x compilers. To achieve user-friendly usage, the SNPector only accepts input from FASTA sequence (Figure 1) and can be operated from a console through simple command line (Figure 2).

Figure 1. Example input naked FASTA sequence.

Figure 2. SNPector command line parameters.

Figure 3. Collective figure to show all Illustrations provided by SNPector.

(A) Circos illustration where other SNPs that have same proprieties are located. (B) Lollipop plot shows values by vertical columns (C) Counter Plot between two values creating a different coloured shade in which more contrast means higher value. (D) Numerical schematic showing the distribution between four values by plotting and scaling colour contrast according to other to values. (E) Heat map between SNP linkage disequilibrium matrix to show how two SNPs are linked. (F) Marginal plot combining column graph and plot, both showing the relationship between two values. (G) Dendrogram with heat-map showing how all SNP are linked to each other. (H) Histogram with box plot to compare visually between two values. (I) Plot illustrating the regression fit of two plotted value. (J) 3D plot of three values. (K) Annotated heat-map showing the plotted value.

SNPector uses different SNP record information collected from NCBI ClinVar (159,184 records), AWESOME (1,080,551 records), and PharmGKB¹⁸ (3,932 records). Ldlink is an online tool that can be used to assess linkage imbalance (LD) throughout ancestral populations and is a popular method of exploring population-specific genetic framework and functionally navigation disease susceptibility areas¹⁹. In SNPector, an Application Program Interface (API) has been programmed to download an LDhap file containing linkage disequilibrium statistics and potentially functional variants for a query variant resulting from the inputted FASTA sequence.

SNPector starts by running BLAST²⁰ software locally to find out the genomic location of a given DNA sequence on human genome. If successful, it retrieves the SNP records located within the query genomic range using NCBI ClinVar. According to retrieved records from the database, the detected SNPs in user-provided queries are marked as wild or mutated. Additionally, more information regarding detecting SNPs records will be retrieved from different implemented databases. This information will be used to generate different illustration figures.

If the process is successfully finished, SNPector will generate four different files: (A) Text file containing the output BLAST result, where the genomic location of the user-defined sequences is predicted; (B) tab delimited file containing SNPs retrieved by NCBI ClinVar located in the same regions; (C) two files regarding specific SNPs information retrieved from AWESOME and PharmGKB databases; (D) different figures depicting SNPs with a similar mutation effect to the detected SNPs located on other genomic regions, SNP linkage disequilibrium, the relationship between SNP, drug, and phenotype (Figure 3).

To achive maximum user-freindly usage, SNPector can be run and controlled by command line. SNPector command line structure (Figure 2) is as follows: A) Python3 compiler; B) scan_dna.py: program main script that contain all functions; C) -blaston / -blastoff: in order to initiate BLAST process to provide sequence alignment against the genome to locate where the sequence is situated, if the -blastoff is chosen it will use previous BLAST results; D) -modescan: to scan the given sequence and find out whether SNP occurs or exists in sequence or not, and -modesearch: to extract all SNPs occur in this range of sequence regardless they are exist or not; E) -circoson: draws a Circos figure to illustrate where SNPs with same properties/effect are located; F) -networkon: in order to link between SNPs, diseases and drugs and produces network HTML file; G) -download: activates the API to download data for identified SNPs from LDlink database; H) -vis: produces different figures and plots; I) GivenSequence.fasta: Tte user-provided sequence in FASTA file format. Any of the previous parameters can be deactivated when replaced with -off.

Use case

In this section we provide an example on how to use SNPector to extract SNPs from a naked given sequence without a reference sequence and how these extracted SNPs are linked to disease development and how they affect drug response. We show how to define the arguments of the SNPector function, interpret the results, and make visualizations.

Data

We use part of an EGFR gene sequence downloaded from NCBI nucleotide database in FASTA format as shown in Extended data: File 1²¹. The EGFR gene FASTA sequence submitted to NCBI contains SNPs that have a clinical effect involved in disease development, such as breast cancer. SNPector uses: (i) NCBI ClinVar database that describes SNP chromosome, position, ID, reference nucleotide, alternative nucleotide, quality, filter, and information to compare and detect SNPs in EGFR sequence that has clinical complications; (ii) PharmGKB database to investigate the SNP effect on disease development and drug response; (iii) AWESOME database to explore SNP effect on phosphorylation, ubiquitination, methylation, and sumoylation sites; and (iv) Ldlink API database of SNP linkage disequilibrium to find out how detected SNPs are linked to other SNPs.

Loading SNPector libraries

SNPector uses different libraries to import, read, and read data and results. os library is used to run BLAST bash script:

import os

time is used to calculate the time that program.

import time

re refers to regular expression. This library sorts and splits input data with function re.split().

import re

itemgetter module is used to sort BLAST data according to identity, mismatch, and p-value.

from operator import itemgetter

From sys library we used sys.argv[] to convert script to command line, which can be run and controlled from the terminal.

import sys

Then we import the Scripts package to visualize the data as follows:

from Scripts.Circos import DrawCircos                                                                              
from Scripts.Network import DrawNetwork                                                                            
from Scripts.Run_BLAST import RunBLAST                                                                             
from Scripts.Extraction import ExtractSNP                                                                          
from Scripts.APIcommands import APIcommands                                                                        
from Scripts.Visualizations import visualization                                                                   
from Scripts.DataVisualization.DownloadWithAPI.LDmatrix import LDmatrix                                            
from Scripts.DataVisualization.DownloadWithAPI.LDhap import LDhap                                                  
from Scripts.DataVisualization.DownloadWithAPI.LDproxy import LDproxy                                              
from Scripts.DataVisualization.CompleteScripts.Ready.ContourPlotWithSeaborn import CounterPlot                     
from Scripts.DataVisualization.CompleteScripts.Ready.CustomLinearRegressionFitSeaborn import LinearReg             
from Scripts.DataVisualization.CompleteScripts.Ready.CustomLollipopPlot import Lollipop                            
from Scripts.DataVisualization.CompleteScripts.Ready.DendrogramWithHeatmapAndColouredLeaves import DendoWithHeatMap
from Scripts.DataVisualization.CompleteScripts.Ready.DensityPlotWithMatplotlib import DenistyPlot                  
from Scripts.DataVisualization.CompleteScripts.Ready.HistogramWithBoxPlot import HistWithBoxPlot                   
from Scripts.DataVisualization.CompleteScripts.Ready.MarginalPlotWithSeaborn import MarginalPlot                   
from Scripts.DataVisualization.CompleteScripts.Ready.ThreeDscatterplot import ThreeDimPlot                         
from Scripts.DataVisualization.CompleteScripts.Ready.UseNormalizationOnSeabornHeatmap import SeabornHeatMap        
from Scripts.DataVisualization.CompleteScripts.Ready.AnnotatedHeatMap import AnnoHeatMap                           
from Scripts.DataVisualization.CompleteScripts.Ready.NumericalSemantics import NumSChem                            
from Scripts.DataVisualization.CompleteScripts.Ready.ThreeDscatterplot import ThreeDimPlot                         
from Scripts.DataVisualization.CompleteScripts.Ready.VolcanoLD import VolLD

SNPector variables

To sort data between the given sequence, Clinvar, AWESOME, BLAST, and PharmGKB, we implement the SNPector variables. This inherits the built-in function open() and nine variables are created as follows:

PharmGKB: data frame describes variant ID, gene name, type of effect, level of evidence, chemicals used to treat the phenotype, and phenotypes;
BLAST_RESULT: data frame lists BLAST output results of alignment of the given sequence against the human genome;
AwesomeDB: data frame lists SNPs chromosome, location, and properties, such as phosphorylation, ubiquitination, methylation, and sumoylation sites;
NCBIclinVar: data frame of SNPs that has clinical impact and involvement in disease;
SNPinDetails: data frame that lists the detected SNPs that SNPector found in the given FASTA sequence;
SNPinDetailsPharmGKB: data frame that lists detected SNPs and its impact on disease development and drug response;
SNPinDetailsAwesome: data frame that lists the properties of detected SNPs;
BLASTfile: function to open and read BLAST output results;
SeqFile: function to read the input file containing the sequence.

Each imported dataset can be found in Extended data²¹.

SNPector building blocks

RunBLAST() takes the file path of FASTA sequence and starts to align the sequence against the human genome and writes the results to BLAST_RESULT.txt (Extended data: File 2²¹).

os.system('./Scripts/blastn -query GivenSequence.fasta -db ./Data/Hum_Genom38 -outfmt 6  - out ./RESULTS/BLAST_RESULT.txt')

ExtractSNP() reads BLAST_RESULT.txt and sort its with itemgetter() according to the identity, length and p-value, then stores the start and end input given sequence (the query) and subject to use later in the extraction step. It also reads the input FASTA sequence file and stores the sequence variable to use in the comparing extraction step. SNPector provides two inspection modes that can be determined from the terminal, Search and Scan. If mode was “-modesearch”, then SNPector begins to extract all SNPs within the query start and end regardless of their existence in the query. In the mode “-modescan”, SNPector will extract only SNPs that exist in the query

SNPector begins to obtain the alternative nucleotides of SNP through the input sequence and obtains the nucleotides that range from SNP position in ClinVar minus the end position of the subject, to the SNP position in ClinVar minus the end position of the subject plus alternative SNP length to ensure the capture of SNPs from the given sequence and also to detect variants with length more than one nucleotide, finally storing it in the “query_nuc_alt” variable.

query_nuc_alt = sequence[int(snp_pos) - int(subject_end):int(snp_pos) - int(subject_end) + len(snp_alt)]

After the process of extraction the result saved to: “FromAwesom.tsv” file (Extended data: File 3²¹), in which SNPector list all other SNPs that have the same effect in different sites in proteins; “FromNCBI.tsv” (Extended data: File 4²¹), which is list of the SNPs that SNPector detects in a given sequence and retrieves from NCBI ClinVar Dataset; “FromPharmGKB.tsv” (Extended data: File 5²¹), which lista the effect of SNPs in disease development and drug response.

APIcommands() imports SNP IDs from “FromNCBI.tsv” and uses Ldlink API to download “LDhap.csv” file (Extended data: File 6²¹), which describes the allele frequency of extracted SNPs, “LDmatrix.csv” file (Extended data: File 7²¹), which shows how far detected SNPs are linked to other SNPs, and a file titles with the SNP id (e.g. rs516316.csv) (Extended data: File 8²¹), which includes additional information, such as minor allele frequency, linkage disequilibrium and distance of other SNPs linked to the detected SNP.

LDmatrix('./RESULTS/FromNCBI.tsv')                                                              
												
LDhap('./RESULTS/FromNCBI.tsv')                                                                 
												
LDproxy('./RESULTS/FromNCBI.tsv')

DrawCircos() uses SNP properties from “FromAwesom.tsv” file (Extended data: File 3²¹) and searches for other SNPs that have the same properties. SNPector then imports pycircos package to draw SNP location on Circos (Figure 3A).

import pycircos

DrawNetwork() draw network using “FromPharmGKB.tsv” (Extended data: File 5²¹) to get the gene name (e.g. EGFR) and by gene name get all SNP that occur in this gene. Using SNP IDs, SNPector obtains disease names caused by these SNPs, and with the disease name SNPector can extract drugs used in treatment for this disease. Finally, with the drug name SNPector can obtain the clinical annotation of the drug. SNPector uses webweb package to draw the network and export it to .html file (Extended data: File 10).

from webweb import web                                                                          
edge_list = Network                                                                             
Web(edge_list).save("./RESULTS/%sVarPhenoDrugNetwork.html" % GeneName)

Visualization() uses data downloaded in the “LDmatrix.csv” file (Extended data: File 7²¹), and the SNP ID file (e.g. rs516316.csv) (Extended data: File 8²¹) to draw other figures (Figures 3B–K).

Discussion

SNPector can collect and retrieve information from the user-provided DNA sequence in the simplest way possible. By integrating different databases into SNPector, it is possible to detect the fluctuations in the abundance of SNPs in query through comparison with known variants of human genome. Such steps are accompanied by the use of online and verified sources to gather previously published details regarding target genomic regions and to generate highly informative visualizations of the recovered information.

Many tools, however, provide SNPs annotation, but they are still limited to the information provided (Table 1). SNPector, on the other hand, provides a new technique that extracts SNP from a naked sequence with no prior information. In addition, another benefit of SNPector is to annotate the discovered SNPs from information retrieved from various known databases.

Table 1. Comparison between SNPector and published SNP annotation toolas.

Software	SNPector	Ensembl VEP	PolyPhen-2	Missense 3D	SIFT	SnpEff	Phyre2
SNP detection from sequence	Yes	No	No	No	No	No	No
Disease and drug annotation	Yes	No	No	No	No	No	No

Conclusion

One of the currently growing medical research paradigms is the diagnosis of genetic virulence that accumulates in our genome causing catastrophic health problems. Detection and diagnosis of genetic variation through skill-less computational tools would help researchers reducing the severity of such health complications and improving well-tailored therapies using discovered and previously known information.

SNPector provides and detects all available information about the disease-related SNPs in the given query with minimum user-provided information. It connects between different available information and produces various illustrations depicting SNP related diseases and treatment network, linked disequilibrium, minor allele frequency, similar SNPs with the same mutation effect and other information.

Software availability

Source code available from GitHub: https://github.com/peterhabib/SNPector

Archived source code as at time of publication: http://doi.org/10.5281/zenodo.3558393²².

License: MIT

Data availability

Underlying data

Homo sapiens chromosome 7, GRCh38.p13 Primary Assembly, Accession number NC_000007.14: https://www.ncbi.nlm.nih.gov/nuccore/NC_000007.14?report=fasta&from=55019017&to=55211628

Extended data

Zenodo: SNPector Supplementary Data, http://doi.org/10.5281/zenodo.3569790²¹.

This project contains the following extended data:

- Supplementary Files 1–10: output files from SNPector for the FASTA sequence use case (NC_000007.14).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

The authors are deeply grateful to Omar S. Abdel-Gaffar, teaching assistant at the College of Biotechnology, Misr University for Science and Technology.

A previous version of this article is available: https://doi.org/10.1101/834580.

Faculty Opinions recommended

References

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Version 2

VERSION 2 PUBLISHED 20 Dec 2019

Author details Author details

Alsamman M. Alsamman
Roles: Investigation, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Sameh E. Hassanein
Roles: Supervision

Ghada A. Shereif
Roles: Validation, Writing – Review & Editing

Aladdin Hamwieh
Roles: Funding Acquisition, Supervision

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

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Published: 20 Feb 2020, 8:2133

https://doi.org/10.12688/f1000research.21556.2

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Published: 20 Dec 2019, 8:2133

https://doi.org/10.12688/f1000research.21556.1

© 2019 Habib PT 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.

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Habib PT, Alsamman AM, Hassanein SE et al. SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence [version 1; peer review: 1 approved with reservations]. F1000Research 2019, 8:2133 (https://doi.org/10.12688/f1000research.21556.1)

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Reviewer Report 08 Jan 2020

Fakher Rahim, Research Center of Thalassemia and Hemoglobinopathy, Health Research Institute, Clinical Research Development Unit, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Approved with Reservations

https://doi.org/10.5256/f1000research.23753.r58286

1.      The rationale for developing the new software tool was not clearly explained. Given lots of closed or similar tools such as SCOPA¹, SIFT, PolyPhen-2, and dbSNP, etc., I was expect more adding value of the present study to describe. One important concern associate with such databases is that these tools should get updated regularly with regards to recent GWAS etc. Ideally, these databases are manually curated.

2.     I have concern about the format of output and interpretation of the results. I think the author should tested this tool even in very small dataset and report that.

3.     One major point is that introducing a new tools need some strong evidences to compete with available and known tools. So the authors should compare the validity and SWOT aspects of this tools with the present tools. So, claiming that “SNPector provides and detects all available information” without a good and clear comparison is only lead to adding a tool to the previous sets of tools.

Is the rationale for developing the new software tool clearly explained?

No
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?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

References

1. Mägi R, Suleimanov YV, Clarke GM, Kaakinen M, et al.: SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.BMC Bioinformatics. 2017; 18 (1): 25 PubMed Abstract | Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Bioinformatics and clinical epidemiology

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.

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Author Response 20 Feb 2020

Peter Habib, Department of Biodiversity and Crop Improvement, International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt

20 Feb 2020

Author Response

First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the ... Continue reading First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the new software tool was not clearly explained so, we edited the introduction and abstract to clarify the goal of SNPector. our idea was to extract the SNPs from a given sequence in FASTA format without the need to do several steps to extract those SNPs and visiting different databases to get the information of detected SNPs. with previous tools, you have to extract the SNPs by aligning against the genome, exporting those detected SNPs in the proper format which is compatible with other tools you need to study SNP effect, migrate the results from database to another to collect all allele frequency and linkage disequilibrium of each SNP, and take the matrices of linkage disequilibrium and excel sheets of allelic frequency and call different scripts and software to visualize the results. in SNPector, with only one command line SNPs extracted, Retrieve information related to drug response and disease development, Collect information of SNP structural effect of different protein critical sites (e.g. phosphorylation sites), downloading linkage disequilibrium and allelic frequency of detected SNP and other linked SNPs on the same chromosome, sorting the data in excel sheets, and finally visualize the downloaded data to be more understandable.

2. We already tested SNPector and the example provided in the paper is itself the testing. we downloaded the part of the EGFR gene, run SNPector, and the results were provided in the paper.

3. We agree with the reviewer so, we edited the discussion section and include comparing examples.
First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the new software tool was not clearly explained so, we edited the introduction and abstract to clarify the goal of SNPector. our idea was to extract the SNPs from a given sequence in FASTA format without the need to do several steps to extract those SNPs and visiting different databases to get the information of detected SNPs. with previous tools, you have to extract the SNPs by aligning against the genome, exporting those detected SNPs in the proper format which is compatible with other tools you need to study SNP effect, migrate the results from database to another to collect all allele frequency and linkage disequilibrium of each SNP, and take the matrices of linkage disequilibrium and excel sheets of allelic frequency and call different scripts and software to visualize the results. in SNPector, with only one command line SNPs extracted, Retrieve information related to drug response and disease development, Collect information of SNP structural effect of different protein critical sites (e.g. phosphorylation sites), downloading linkage disequilibrium and allelic frequency of detected SNP and other linked SNPs on the same chromosome, sorting the data in excel sheets, and finally visualize the downloaded data to be more understandable.

2. We already tested SNPector and the example provided in the paper is itself the testing. we downloaded the part of the EGFR gene, run SNPector, and the results were provided in the paper.

3. We agree with the reviewer so, we edited the discussion section and include comparing examples.
Competing Interests: No competing interests were disclosed. Close
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Author Response 20 Feb 2020

Peter Habib, Department of Biodiversity and Crop Improvement, International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt

20 Feb 2020

Author Response

First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the ... Continue reading First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the new software tool was not clearly explained so, we edited the introduction and abstract to clarify the goal of SNPector. our idea was to extract the SNPs from a given sequence in FASTA format without the need to do several steps to extract those SNPs and visiting different databases to get the information of detected SNPs. with previous tools, you have to extract the SNPs by aligning against the genome, exporting those detected SNPs in the proper format which is compatible with other tools you need to study SNP effect, migrate the results from database to another to collect all allele frequency and linkage disequilibrium of each SNP, and take the matrices of linkage disequilibrium and excel sheets of allelic frequency and call different scripts and software to visualize the results. in SNPector, with only one command line SNPs extracted, Retrieve information related to drug response and disease development, Collect information of SNP structural effect of different protein critical sites (e.g. phosphorylation sites), downloading linkage disequilibrium and allelic frequency of detected SNP and other linked SNPs on the same chromosome, sorting the data in excel sheets, and finally visualize the downloaded data to be more understandable.

2. We already tested SNPector and the example provided in the paper is itself the testing. we downloaded the part of the EGFR gene, run SNPector, and the results were provided in the paper.

3. We agree with the reviewer so, we edited the discussion section and include comparing examples.
First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the new software tool was not clearly explained so, we edited the introduction and abstract to clarify the goal of SNPector. our idea was to extract the SNPs from a given sequence in FASTA format without the need to do several steps to extract those SNPs and visiting different databases to get the information of detected SNPs. with previous tools, you have to extract the SNPs by aligning against the genome, exporting those detected SNPs in the proper format which is compatible with other tools you need to study SNP effect, migrate the results from database to another to collect all allele frequency and linkage disequilibrium of each SNP, and take the matrices of linkage disequilibrium and excel sheets of allelic frequency and call different scripts and software to visualize the results. in SNPector, with only one command line SNPs extracted, Retrieve information related to drug response and disease development, Collect information of SNP structural effect of different protein critical sites (e.g. phosphorylation sites), downloading linkage disequilibrium and allelic frequency of detected SNP and other linked SNPs on the same chromosome, sorting the data in excel sheets, and finally visualize the downloaded data to be more understandable.

2. We already tested SNPector and the example provided in the paper is itself the testing. we downloaded the part of the EGFR gene, run SNPector, and the results were provided in the paper.

3. We agree with the reviewer so, we edited the discussion section and include comparing examples.
Competing Interests: No competing interests were disclosed. Close
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VERSION 2 PUBLISHED 20 Dec 2019

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Fakher Rahim, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Ka-Chun Wong, City University of Hong Kong, Kowloon Tong, Hong Kong, China
Tim Kacprowski, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany

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17 Views

16 Mar 2021 | for Version 2

Tim Kacprowski, Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany

17 Views Cite this report Responses(0)

Not Approved

This manuscript aims to introduce a new, particularly user-friendly software to retrieve information on SNPs based solely on the input of query sequences. Unfortunately, it fails to do so convincingly on many levels. Below I list a number of comments, in no particular order.

The title introduces the term "naked sequence". It remains unclear throughout the manuscript what this is supposed to be.
There are many problems with wording and language. The whole manuscript should be thoroughly checked for typos, wording, grammar, semantics, etc. These are really too numerous to list them all.
Many of them lead to scientifically questionable statements (e.g. "sub-atomic subtleties of disease origin").
While the introduction lists a couple of already existing SNP analysis tools, it does not explain what SNPector can do better and why it is needed.
The authors stress "user-friendliness" multiple times. Yet, using SNPector is far from user friendly. The authors claim that user-friendliness is enhanced by running SNPector via the command line, however, the target audience seems to be biologists who might not be familiar with the command line interface. Furthermore, SNPector is only available as a Python package, suggesting the user needs sufficient knowledge of python. Apparently, the user even hast to handle the import of basic packages such as "re" etc. himself when using SNPector?! A user-friendly tool should take care of these things, be available preferably also in other languages, or even as a web-service.
While the authors claim that SNPector works without a reference sequence, the first requirement that is listed in the GitHub repository is to download the human genome reference sequence.
It remains unclear if or how SNPector identifies SNPs present in the input sequence or if simply queries databases for generally existing SNPs within the region covered by the input sequence. It does not seem to care if a particular SNP is actually present in the query sequence or not.
What is the purpose of SNPector? It seems to be simply to annotate a given query sequence. What about the analysis of multiple samples? Introduction and conclusion seem to suggest this, although it cannot be derived from the rest of the manuscript. The authors should harmonize the text in order to refrain from overselling their tool, yet to achieve consistent description and motivation.
The captions of Figures 1, 2, and Table 1 should be expanded.
Figure 9 as well as its caption are not informative. The authors should provide this figure in better quality (resolution) and describe the actual content of the figure instead of simply enumerating the types of plots that are shown.
Any potential results of the use case are not discussed or interpreted. What is the point of this use case? What insights can actually be derived?
The intermediary data obtained from other databases through SNPector lacks any meaningful description or column headers or the likes. In its current form, it seems worthless for the user, as the user has to go through the manuals of these external data sources in order to even understand what is presented.
The hardware requirements for SNPector are misleading (Why wouldn't it run on i5 cores?) and there is no hint towards the runtime of the presented workflow.
A significant part of the manuscript is simply listing python import statements. Seems out of place.
What are "skill-less" computational tools?
In the conclusion, the authors mention "genetic virulence". Do they mean virulence factors? How are they relevant in the context of SNPector? Why is this not addressed earlier in the manuscript?

Is the rationale for developing the new software tool clearly explained?

No
Is the description of the software tool technically sound?

No
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?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Data Science in Biomedicine, Bioinformatics, Genetic Epidemiology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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8 Views

08 Mar 2021 | for Version 2

Ka-Chun Wong, Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China

8 Views Cite this report Responses(0)

Approved With Reservations

The authors proposed a practical software for SNP inspection on a given sequence in human. The authors proposed a software which has integrated some bioinformatics tools together as a single portal for SNP inspection on a given sequence in human. Since it is based on the developed tools, there is not any need for benchmark comparisons. Methodology novelty is also not expected. The main concerns that I would have are mainly from the software utility perspective as follows:

Major Comments

The proposed software heavily relies on the other existing bioinformatics software such as BLAST and existing databases such as ClinVar. I was wondering how the proposed software can be automatically updated if the software dependency has been changed?
The authors have released the source code on GitHub. However, some users may simply prefer using it on the web. I was wondering if the authors could wrap the software as a webserver to be deployed somewhere for user-friendliness.
There are many SNP pathogenicity detection software references missing in the manuscript.
The software was written in Python. However, some users may use R (e.g. Bioconductor). The authors may wish to discuss it and the possible solutions for it.

Minor Comments

The authors may wish to specify that the study is for human in the title.
The term “naked sequence” looks weird to me. I am not sure if it is appropriate since I thought it referred to nucleosome-free sequence.
There are typo errors on GitHub (e.g. diseas)

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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Bioinformatics

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10 Views

01 May 2020 | for Version 2

10 Views Cite this report Responses(0)

Approved

The authors' efforts to resolve the reviewer's concerns are acceptable and satisfying.

Competing Interests

No competing interests were disclosed.

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.

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37 Views

08 Jan 2020 | for Version 1

37 Views Cite this report Responses(1)

Approved With Reservations

Is the rationale for developing the new software tool clearly explained?

No
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?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Bioinformatics and clinical epidemiology

Respond to this report

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Author Response

20 Feb 2020

Peter Habib, Department of Biodiversity and Crop Improvement, International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt

First of all, we thank the reviewer for his insightful comments/suggestions that have improved the quality of the manuscript.

1. We agree with the reviewer that the rationale for developing the new software tool was not clearly explained so, we edited the introduction and abstract to clarify the goal of SNPector. our idea was to extract the SNPs from a given sequence in FASTA format without the need to do several steps to extract those SNPs and visiting different databases to get the information of detected SNPs. with previous tools, you have to extract the SNPs by aligning against the genome, exporting those detected SNPs in the proper format which is compatible with other tools you need to study SNP effect, migrate the results from database to another to collect all allele frequency and linkage disequilibrium of each SNP, and take the matrices of linkage disequilibrium and excel sheets of allelic frequency and call different scripts and software to visualize the results. in SNPector, with only one command line SNPs extracted, Retrieve information related to drug response and disease development, Collect information of SNP structural effect of different protein critical sites (e.g. phosphorylation sites), downloading linkage disequilibrium and allelic frequency of detected SNP and other linked SNPs on the same chromosome, sorting the data in excel sheets, and finally visualize the downloaded data to be more understandable.

2. We already tested SNPector and the example provided in the paper is itself the testing. we downloaded the part of the EGFR gene, run SNPector, and the results were provided in the paper.

3. We agree with the reviewer so, we edited the discussion section and include comparing examples.

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Competing Interests

No competing interests were disclosed.

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

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SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence

Abstract

Keywords

Introduction

Methods

Functions

Operation

Workflow

Figure 1. Example input naked FASTA sequence.

Figure 2. SNPector command line parameters.

Figure 3. Collective figure to show all Illustrations provided by SNPector.

Use case

Data

Loading SNPector libraries

SNPector variables

SNPector building blocks

Discussion

Table 1. Comparison between SNPector and published SNP annotation toolas.

Conclusion

Software availability

Data availability

Underlying data

Extended data

Acknowledgements

References

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