Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

An Integrated SNP Mining and Utilization (ISMU) Pipeline for Next Generation Sequencing Data

  • Sarwar Azam,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • Abhishek Rathore,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • Trushar M. Shah,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • Mohan Telluri,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • BhanuPrakash Amindala,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • Pradeep Ruperao,

    Affiliations Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India, School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia

  • Mohan A. V. S. K. Katta,

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

  • Rajeev K. Varshney

    Affiliation Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

An Integrated SNP Mining and Utilization (ISMU) Pipeline for Next Generation Sequencing Data

  • Sarwar Azam, 
  • Abhishek Rathore, 
  • Trushar M. Shah, 
  • Mohan Telluri, 
  • BhanuPrakash Amindala, 
  • Pradeep Ruperao, 
  • Mohan A. V. S. K. Katta, 
  • Rajeev K. Varshney


Open source single nucleotide polymorphism (SNP) discovery pipelines for next generation sequencing data commonly requires working knowledge of command line interface, massive computational resources and expertise which is a daunting task for biologists. Further, the SNP information generated may not be readily used for downstream processes such as genotyping. Hence, a comprehensive pipeline has been developed by integrating several open source next generation sequencing (NGS) tools along with a graphical user interface called Integrated SNP Mining and Utilization (ISMU) for SNP discovery and their utilization by developing genotyping assays. The pipeline features functionalities such as pre-processing of raw data, integration of open source alignment tools (Bowtie2, BWA, Maq, NovoAlign and SOAP2), SNP prediction (SAMtools/SOAPsnp/CNS2snp and CbCC) methods and interfaces for developing genotyping assays. The pipeline outputs a list of high quality SNPs between all pairwise combinations of genotypes analyzed, in addition to the reference genome/sequence. Visualization tools (Tablet and Flapjack) integrated into the pipeline enable inspection of the alignment and errors, if any. The pipeline also provides a confidence score or polymorphism information content value with flanking sequences for identified SNPs in standard format required for developing marker genotyping (KASP and Golden Gate) assays. The pipeline enables users to process a range of NGS datasets such as whole genome re-sequencing, restriction site associated DNA sequencing and transcriptome sequencing data at a fast speed. The pipeline is very useful for plant genetics and breeding community with no computational expertise in order to discover SNPs and utilize in genomics, genetics and breeding studies. The pipeline has been parallelized to process huge datasets of next generation sequencing. It has been developed in Java language and is available at as a standalone free software.


Next Generation Sequencing (NGS) technology has changed the research landscape of genomics especially crop genomics since the last few years [1][4]. Illumina, Life Technologies' SOLiD and Ion Torrent sequencing platforms have been used for rapidly identifying SNPs and other marker studies in crops. These platforms usually produce short reads of 50–150 bp which have been preferred for SNP identification. Rapid technological advances in recent times enabled NGS technologies to provide significantly higher throughput at lower cost. However, the cost involved in sequencing experiments is very high, especially, for species with large genomes or species for which the reference genome is not available (orphan crops). In such cases, transcriptome sequencing from more than one genotype is a first choice for plant genetics and molecular breeding community to decipher SNPs. The advantage of this approach is that the identified SNPs are mostly located in single copy genes, which are a pre-requisite for SNP marker analysis. In various model or major crop species, transcriptome sequencing has been used for allele discovery and gene expression analysis [5][9]. Here, the limited number of SNPs detected in the genes was due to selection constraints in coding regions which result in finding only few thousand useful markers. To overcome this limitation, alternative approaches have been developed. These approaches use NGS technologies in combination with complexity reduction technologies [10]. These complexity reduction technologies cover the entire genome not limited to protein coding regions. Hence, other single-copy sequences can be surveyed for SNPs as well. Complexity reduction technologies are based, for example, on the selective sequencing of a DNA fraction derived from the digestion with methylation sensitive restriction enzymes [11][14], the pre-amplification with specific AFLP (amplified fragment length polymorphisms) primer combinations [15] or the use of the RAD (restriction-site associated DNA) sequencing technology [16][18]. In comparison to the transcriptome-based approach, the complexity reduction technologies have an advantage that they find use more or less independent of the genome size. However, NGS technology has been used recently for sequencing and whole genome re-sequencing projects with an objective to mine for a large number of SNPs towards constructing haplotype maps, exploring diversity within-species and performing genome-wide association studies (GWAS) for trait mapping [19][21].

Identified SNPs are highly effective only when they are distributed throughout the genome and genotyped for the population of interest or for a set of germplasm. In general, SNP discovery using NGS is limited to the number of lines assayed or sequenced. Genotyping large number of SNPs facilitates various genetic analyses (e.g. phylogenetic analysis, ultra-dense genetic mapping and genotype/phenotype association studies) and important applications (e.g. cultivar identification, marker-assisted selection). The best approach towards the SNP marker discovery for genotyping is to compare fully sequenced genomes from individuals of a given species. However, in case full reference sequence is not available, reads could be assembled to derive a mimic reference sequence and genome-wide SNPs can be identified [22]. In recent years, several such orphan crops have been enriched with full or partial reference genome sequence information. Comparing the re-sequencing data with the reference genome sequence provides a large number of SNPs throughout the genome and has been described in various crop species such as chickpea [23], maize [24], rice [25], and soybean [26]. Identification of SNPs allows their application as markers in various genetic analyses. These genetic analyses further require genotyping of SNP markers on a full set of the material/population. Even though the genotyping methods are limited by the cost and time for scoring SNPs, there has been a steady development of various high-throughput and low cost automated genotyping platforms. These platforms were primarily developed for human genomics, nevertheless, they have been adopted and used for plant species such as chickpea [23], [27], maize [28], pigeonpea [29], rice [30] and wheat [31]. They are very efficient in handling large number of samples and can genotype one to one million SNPs [32], [33] in parallel. Some popular technologies like TaqMan and SNPlex™ from Applied Biosystem/Life technologies (Carlsbad, CA, USA), array based technologies like GoldenGate and Infinium technology from Illumina (San Diego, CA, USA) and PCR based technology like KASP from LGC limited (Teddington, Middlesex, UK) are the most sought-after options for molecular breeders towards genotyping studies owing to low cost, high-throughput and automated workflow.

In general, SNPs could be utilized to enrich the genomic resources for a species in mainly two steps, first, is to identify DNA sequence polymorphisms (marker discovery) and then, second is to assay the identified SNP markers (genotyping) across the segregating population(s) or germplasm set. In this context, several data analysis pipelines such as ngs-backbone [34], SeqGene [35], Games [36], inGap [37] and GATK [38] have become available that process data stepwise manner to identify SNPs from NGS data. These pipelines vary in the type of input data for SNP analysis. For example, some pipelines start with raw data, some with processed data and some from alignment data. These pipelines in general provide a list of SNPs between the reference genotype and sample. In fact, low concordance is observed amongst the SNP of multiple SNP calling pipelines. These are mainly due to diverse integrated alignment tools and SNP prediction methods. Furthermore, almost all pipelines available as open-source are command line based and work only on Linux platform. Moreover, the identified SNPs need to be processed and filtered further to design assays for using them as marker-genotyping assays. In fact, to the best of our knowledge, the step of producing input file for the filtered and high-quality SNPs for assay designing is not available in any pipeline. Therefore, the present study was undertaken with an overall objective to develop a graphical user interface (GUI) based, automated SNP discovery pipeline from NGS data. We have called it as Integrated SNPs Mining and Utilization (ISMU) pipeline. The pipeline identifies SNPs from the NGS data of samples and extracts informative SNPs for genotyping platforms such as Illumina (BeadXpress, GoldenGate, Infinium) and KASP. ISMU supports single end (SE) as well as paired end (PE) data and lists the informative SNPs with polymorphism information content (PIC) values. To facilitate the analysis in small laboratories with limited computation facilities, the pipeline is distributed on request as a virtual machine image as well as on Compact Disk (CD).


ISMU is an easy to use GUI based pipeline for SNP discovery useful for experimental biologists, geneticists and plant molecular breeding community. It allows researchers to analyse NGS data generated by Illumina, Life Technologies (SOLiD) or reanalyze already deposited data from SRA (Sequence Read Archive) of NCBI. It is highly customizable and integrates popular command line based tools for alignment and SNP prediction with support for both SE and PE data in a standard FASTQ format [39]. It is designed to run on 64 bit desktop machines. The support for multi-threading in ISMU enables the user to analyze high volume data. The pipeline provides high quality SNPs that could be used for genotyping assay development for KASP and Illumina platform. All the result files are flat text files except a spreadsheet namely “called_allele_data.xls”. The pipeline additionally generates a list of INDELs if BWA/Bowie2 was used for alignment.

Features of the pipeline

Identification of SNPs is a multi-step process involving various tools for pre-processing the data, alignment against the reference and identifies variations subject to filters. The work-flow (Figure 1) could be broadly categorized into the six steps as follows: (i) data import, (ii) data pre-processing, (iii) sequence alignment or mapping short reads onto the reference genome, (iv) SNP discovery, (v) visualization, and (vi) generation of genotyping assay input files. All these steps have been built the automated ISMU pipeline for ease of use. ISMU offers a wide choice of tools at every step with default parameters. Multiple options enable comparison across different (alignment and SNP calling) methodologies and thereby arrive at a concordant set of SNPs for targeted genotyping.

Figure 1. The work-flow of the ISMU pipeline.

The work-flow of the ISMU pipeline is mainly divided into three steps: (A) Data import, quality pre-processing, (B) Sequence alignment and SNP discovery, and (C) Visualization and generation of input files for genotyping assay.

Importing the data.

The pipeline requires input files in the FASTQ format [40]. It accepts both paired and single end sequencing data. However, several FASTQ variants exist (eg. Illumina produces NGS data in FASTQ format with different quality score ranges -Illumina 1.3, llumina 1.5 and Illumina 1.8) and it would be tedious to support them all. To circumvent this inconsistency, the pipeline accepts only phred encoded FASTQ (Sanger FASTQ) files as input. Additionally, a reference sequence file in FASTA format is required which would be used as template to align the input datasets.


The pipeline processes the input FASTQ files to assess and produce an overview of quality distribution of the dataset. Pre-processing enhances the quality of the input data by filtering and trimming reads. It filters off low quality reads followed by trimming low quality regions at the end of the residual reads. This pre-processing step is executed with a Perl script which reads the input data and considers the bases of reads as low quality, if the phred score [40] is less than 20. The script counts the number of low quality bases in a read and if the percentage of such bases is greater than 30%, the read is flagged as a low quality and discarded. Next, the average phred score at each position of the read and average phred score of whole dataset are computed independently. These scores are used as thresholds to trim the low quality bases of reads either from one end (5′ or 3′) or both the ends depending upon the average quality of bases at the ends. The threshold is set to 3 phred score units less than the average phred score. The number of bases to be trimmed from the ends is dynamically determined at runtime based on the sudden change of average phred scores greater than one phred score unit above the threshold. Paired end data is further processed by discarding all the reads not found in pairs. As a result, a FASTQ file with high quality reads is generated for downstream processing. Summary statistics of the pipeline describing the quality of raw data, trimming coordinates and the number of filtered reads is generated. The total run time to process the reads in this step depends on the amount of input data and the type of data (SE or PE). This pre-processing step is optional.

Sequence alignment.

The pre-processed high quality reads are mapped onto the reference sequence. Alignment tools namely BWA [41], Maq [42], Bowtie2 [43], NovoAlign [44], and SOAP2 [45] have been embedded into the pipeline. The user could opt for any of the above mentioned tools and provides the option to change the default parameters. The default values for the parameters have been optimized and would be useful for users who are not familiar with the tools. This step starts with indexing of reference sequence for fast alignments followed by the mapping of the reads resulting in an alignment output in SAM format. By default Maq and SOAP2 alignments are in MAP format and were converted to SAM format using the Perl script “” [46]. The alignment statistics reported by the tools are available in the log file generated and the summary report of the pipeline. The alignment statistics provide details on the extent of mapping and guide the user on how the parameters affect the mapping process. Alternately, the SAM format alignment files generated from alternate mapping tools could be used for downstream processing in ISMU.

SNP identification.

Good alignment with high read depth is an essential prerequisite with NGS data for efficient SNP discovery irrespective of sequencing platforms. The pipeline provides two different SNP calling methods namely: (i) SAMtools [47] and (ii) Coverage based Consensus Calling (CbCC) [48]. If the SAMtools method is selected, the SAM format files are converted to BAM format and bcftools used to call variations (between reference and samples) in VCF format [49]. The pipeline utilizes this information to find the confident polymorphism between the samples as well as between reference and samples. The SAMtools SNPs are further confirmed by the application of a custom filter as follows. The stack of bases at a given SNP position that satisfy a quality threshold of phred score >20 are derived. The frequency of the resultant bases is used to arrive at a consensus base. This consensus base could be a major base or an ambiguous base representing heterozygosity. These bases are then compared across genotypes to infer confident SNP. Resultant SNPs are reported with frequency information on the major base called and their read depth. In case SAM format alignments are not available by default, like when using Maq and SOAP aligners, SOAPsnp [50] and Maq (“”) are used instead of SAMtools respectively.

In the CbCC method, the pipeline reads the SAM files of samples and extracts the pileup information at all positions. This information was used to calculate and compare the consensus base or consensus allele for each sample at every corresponding position of reference. If the consensus base differs between the samples at that position then it is reported as an SNP. The pipeline computes the allele frequency at every position of the aligned sequence. If this allele frequency is above a Fmajor threshold of 0.66, it is considered as a major/consensus base [48].

Finally, SNPs between the samples (pairwise) as well as against the reference sequence are reported. Additionally, a comprehensive non-redundant set of SNPs across all the samples as well as the reference is also reported as SNP genotyping matrix. All the reported SNPs are described with their position on reference sequence, major bases and the corresponding confidence ratio in each of the samples provided. These statistics further provide a confidence measure in selecting the SNPs for the design of genotyping assays.


Although this step is optional, visualization of alignment and distribution of SNPs identified in samples would help highlight any possible sequencing errors or artefacts. The pipeline is integrated with two visualization tools namely, Tablet [51] and Flapjack [52]. The Tablet is used for visualizing alignment of reads on the reference sequence and to observe the called alleles at the SNP positions. It accepts SNP information in gff3 format to directly navigate to SNP positions on the alignment display [53]. The user has an option to select a set of SNP positions (i.e., SNPs between any two samples) from the drop down list on the results window to view in Tablet. Flapjack is another Java based graphical genotyping software that could be used to visualize the SNPs or the allele distribution on each chromosome amongst the samples. A haplotype can be viewed and the user could also group, cluster or sort the genotypes/lines of similarity with other lines.

Input file for genotyping assay.

This step of the pipeline generates an input file for the design of genotyping assays. The pipeline checks for various criteria for selecting SNPs suitable for genotyping. First, the SNP should have complete flanking sequences on either side. Second, the flanking sequences must not contain any ambiguous bases like “N”, “K”, “R”. Third, the flanking sequences should not contain any other SNP. An SNP satisfying these criteria are preferred for genotyping assay (However, flanking sequence length is different for KASP and Illumina genotyping platforms). All such potential SNPs are filtered to prepare a preliminary input file for genotyping assay development. One can exclude less informative SNPs either by visual inspection or on the basis of PIC value, so that highly informative SNPs could be used further in genotyping experiments.

Pipeline output

The output generated by the pipeline include a summary report, SNP information for a pair wise combination of samples, amongst the samples, input files for KASP genotyping and preliminary input file for ADT scoring for Illumina genotyping. All the files are in a tab separated text file and a spread sheet with the file name “called_allele_data.xls” is also produced. Other than the log and vcf files, many other intermediate files like SAM and BAM files for each sample and filtered high quality FASTQ datasets are generated, which could be accessed in standalone ISMU. Input files could also be visualized.

Evaluation of the pipeline

The pipeline was evaluated for its performance, accuracy and speed using two genomic and one transcriptomic datasets.

Pre-processing and alignment.

The genomic dataset included whole genome re-sequencing (WGRS) data from four genotypes of chickpea (Pistol, Slasher, Hat Trick and Genesis 90) and the RAD sequencing data from ten genotypes of chickpea (ICCV 03107, ICC 4918, ICC 4930, ICC 4958, ICC 5270, ICCV 05530, ICC 5810, ICC 5912, ICC 6263 and ICC 8261). The transcriptomic dataset included RNAseq data from two peanut genotypes (HuaU12 and HuaU606). After pre-processing, the reads passing filter criteria in the WGRS, RAD and RNAseq datasets were 97.11% (125.07 million), 95.39% (74.22 million) and 98.18% (6.73 million) respectively. WGRS and RAD datasets were aligned to chickpea genome while RNAseq dataset was mapped against unigenes of peanut. On an average, 92.68% (Table 1), 90.5% (Table 2) and 80.61% (Table 3) of reads mapped onto the reference sequences from WGRS, RAD and RNAseq datasets respectively.

Table 1. Details about whole genome re-sequencing (WGRS) dataset used for evaluation of the pipeline.

Table 2. Restriction site associated DNA (RAD) sequence dataset used for evaluation of the pipeline.

Variant detection.

A total of 579,813 SNPs were identified from WGRS data (Figure 2) against the reference. A subset (62,291) of these SNPs in each genotype has shown polymorphism with reference genotype, CDC Frontier. Maximum variation (252,041 SNP) was found between Hat Trick and Slasher while minimum variation (145,415 SNP) was observed between Hat Trick and Genesis90. In case of RAD dataset, maximum number of SNPs with reference sequence was observed in ICC 4930. The total numbers of SNPs were observed to be in the range of 442 to 1151 between any pair of genotypes (Table 4). An SNP matrix consisting of 28,348 polymorphic positions across all the genotypes including reference was derived. Alleles in several genotypes were not found to be typed and hence they were regarded as missing due to no coverage or less coverage. This is a usual characteristic of RAD sequencing data and so one could opt for imputation to overcome this limitation [54]. In case of RNAseq dataset, HuaU606 shows more SNPs with reference than HuaU12 (Table 3). However a total of 13,294 SNPs were called between these two genotypes.

Figure 2. A snapshot on SNPs in four chickpea genotypes compared to the reference genome.

The Venn diagram shows distribution of SNPs detected between four genotypes (Pistol, Hat Trick, Slasher and Genesis 90). The genotype CDC Frontier was used as a reference sequence. For instance, a total of 95,329 SNPs were found to be concordant between Pistol and Hat Trick genotypes. Similarly, amongst all the four genotypes 62,291 SNPs were found to be in common.

Table 4. Pairwise SNP distribution between genotypes identified in RAD dataset.

Profiling of ISMU

Allele calling is the most memory intensive part of the pipeline. A computational profile of the pipeline with the three datasets was recorded in order to observe the peak memory consumption, disk-space allocated and the time taken for the analysis to complete. In each case it was tested with 18 processors on a Linux based desktop machine with 48 Gb RAM (Table 5). The RNAseq dataset is found to be analyzed quicker than RAD followed by WGRS dataset. The reference sequences of RNAseq dataset are comparatively much smaller than the reference sequences used with WGRS and RAD datasets. It is apparent that more the input data, more time taken and hence more disk space required. The peak memory required was found to be on a higher side with WGRS and RAD datasets in contrast to RNAseq dataset. This is because the lengths of reference sequences in the case of RNAseq dataset are much smaller than WGRS and RAD datasets. In addition, WGRS and RAD datasets are 17 and 3 times larger than RNAseq dataset respectively and hence the memory requirement scales up accordingly. Interestingly RAD dataset consumes memory resources similar to WGRS inspite of being smaller in size to WGRS dataset. This is due to the fact that more number of genotypes are included in RAD dataset in comparison to WGRS dataset. Therefore we conclude that the number of genotypes as input, the reference chromosome size and the amount of data affect the RAM required the pipeline. Hence if one has more data and/or have more genotypes to be analyzed, we advise to run the ISMU pipeline accordingly on resourceful machines. However, transcriptome data or smaller genomes or a less number of genotypes of larger genomes could be efficiently handled on a desktop.

Table 5. Run time profile of the ISMU pipeline with three datasets (WGRS, RAD and RNAseq).


ISMU is a novel pipeline for SNP mining packed with unique features that are superior in comparison to other pipelines (Table 6). In general, the existing pipelines for SNP mining are command-line based which require technical expertise and therefore are not user friendly. Hence, ISMU includes a graphical user interface (GUI) to ease the analysis for the molecular breeder community as they are not familiar with technical nuances of Linux operating system and command line tools. Integration of robust pre-processing and visualization features especially flapjack for haplotype view makes it a unique pipeline of its own kind. Furthermore, the existing pipelines identify and report SNPs between reference and sample, however ISMU directly reports SNPs between two samples as well and provides a non-redundant list of SNPs amongst the samples. This could be used to compare and contrast allelic differences between the genotypes. The CbCC method for SNP detection is a reference free method useful for such plant species, where reference genome is not available. The detected SNPs were also provided as a spreadsheet containing heterozygosity, PIC value and allele distribution of each marker in different genotypes. This would help the breeding community to prioritize SNPs for genotyping. SNPs with high PIC values tend to show a high level of polymorphism in a given germplasm set which is desired for linkage mapping, QTL mapping and diversity studies, thus find applications in molecular breeding. In fact, the pipeline outputs the frequency of all the alleles present in samples and provides breeders to exploit this information for mining novel and rare allelic variants, which may have several functional significance for trait association mapping in crop plants. Minor allele frequency (MAF) for SNP locus can be inferred from the frequencies of the alleles directly. Alternately, lower value of heterozygosity at SNP locus provides a clue in finding locus with minor allele in the samples. Additionally, SNP data satisfying KASP genotyping platform criteria are filtered and provided in a KASP input format for conversion of SNPs to KASP assays. Similarly, SNPs satisfying criteria of the Illumina genotyping platform were also provided in a preliminary input format. This can be directly submitted to Illumina's online Assay Designing Tool (ADT) to score for predicting the success information and validation status [55]. Further if the markers qualify the threshold ADT score, they can be used for OPA (Oligo Pooled Assay) development on Bead Chips for different machine of Illumina (BeadXpress, Infinium Assay).

Table 6. Comparison of key features of the ISMU pipeline with similar pipelines.

In fact, this pipeline was initially developed for transcriptome data and later extended for genomic data. The pipeline has been designed to run on resource constrained environments (small laboratories) enabling NGS data to be processed even with very low amount of RAM. Installation of software and its dependency tools for processing NGS data and the downstream analysis are a daunting task for many researchers especially for a molecular breeder who may have little computational expertise. Therefore ISMU is also provided as a ready to use Virtual machine image facilitating the user to quickly get started on Desktop/Workstation and comfortably add more analytic power if required.

Selection of tools

Open source NGS tools have been selected carefully and integrated in the ISMU. The pipeline presents the user with numerous options for alignment and SNP calling that makes it versatile and unique in comparison with similar pipelines. Multiple options help the user to reanalyze with different tools to find the best or concordant results. However each tool has its pros and cons. In case, one has high quality data available, the results with different tools would appear similar, but with low quality (shallow coverage) data the differences in results are evident [48].

Sequence alignment is one of the major steps that greatly affect the marker discovery. In this step the mapping tools use heuristics to align short reads to the reference sequence as exhaustive and accurate algorithms would not be computationally feasible. Initially, small regions (seed) in reads would be identified against the reference sequence where the location of the best match is most likely to be found. After identification of a smaller subset of possible mapping locations, slower but more accurate alignment algorithms such as Smith-Waterman are run on the limited subset [56]. Algorithms that are used to search the small set of potential alignments in the reference sequence can be broadly classified into two main categories - hash table and suffix tree/array. Hash based algorithms demand more resources (RAM) and computational time (ELAND/Maq/SOAP). Suffix arrays in combination with compression techniques like Burrow Wheeler Transformation (BWT) and FM index have been used to generate both space and time efficient alignment programs (BWA/BOWTIE/SOAP2), outperforming hash based methods [56], [57]. In total, one hash based software, Maq and four BWT based software, Bowtie2, BWA, NovoAlign and SOAP2 have been integrated into the pipeline. All tools are capable of handling short reads as well as SE and PE data; some software like BWA also capable for long reads. Hence it is advised to use BWT based software options in the pipeline because of quick run time and lower memory requirement.

The ISMU pipeline integrates four SNP calling programs. SAMtools (v0.1.19) [47] has been selected for variant calling as it is the most popular SNP calling program evident from the literature. SAMtools accepts alignment input in the SAM format produced by three alignment software namely BWA, Bowtie2 and NovoAlign. However, alignments using Maq and SOAP2 are not reported directly in SAM format, rather in Map format, but could be converted to SAM format. Map format files when converted to SAM format, does not contain header information. This header could be regenerated from the reference and supplied to SAMtools, to produce BAM files that could be used for SNP calling. However such BAM file does not work properly with SAMtools. Hence, as a workaround, Maqsnp (a script in Maq package) and SOAPsnp [50] software were integrated for consensus calling and to predict SNPs from and SOAP and Maq alignment respectively.

Alternately, another SNP calling method, Coverage based consensus calling (CbCC) [48] is included and could be opted to use with any of the alignment software integrated into ISMU. It uses alignment files in SAM format, either produced from alignment tools or converted into SAM format from other formats. On the other hand, CbCC has been written in Perl. The method generates a list of SNPs by directly comparing between two genotypes and has been very helpful in dealing with species lacking a finished reference genome sequence.

Comparison with existing pipeline/software

Several open source pipelines for NGS data analysis are available for the research community. But not all of them support the analysis from raw NGS data. Very few pipelines like ISMU start with preprocessing of raw NGS data while pipelines such as GATK, GAMES, Atlas2 (Table 5) are designed to work starting with alignment files such as BAM/SAM. Many pipelines do not include the pre-processing steps which affects the false discovery rate of SNP predicted. However, in ISMU, the pre-processing step is an optional but recommended feature one could choose to avail depending on the quality of the input data. ISMU is a one-stop-shop tool for SNP analysis utilizing raw data as input with a user friendly interface (GUI) hiding the computational details. However, Atlas and inGAP are the only other pipelines with GUI. In fact ISMU is intended to assist genetics and breeding community who finds working with Linux/command line tedious. Therefore, a Virtual box image of the pipeline is provided to avert installation issues on windows as well Linux platforms. ISMU offers a wide range of alignment tools (Bowtie2, BWA, Maq, NovoAlign, and SOAP2) and SNP prediction methods (SAMtools, CbCC, SOAPsnp, Maq), a combination seldom available in other pipelines and hence is a unique feature in itself. In general the SNP calling tools provide SNPs with a quality/probability score, whereas ISMU provides a confidence ratio which reflects the read depth. This confidence ratio would be helpful in tagging high quality SNPs for genotyping studies. In fact, ISMU is the only pipeline which has an integrated facility for generating input file for developing assays for genotyping. The pipeline provides a non-redundant set of potential SNPs that could be used as an input file for KASP assay as well as Illumina based OPAs development for genotyping. While other pipelines provide SNPs against the reference only, ISMU in addition provides SNPs in pairwise combinations of genotypes as well as SNP matrix which is equivalent to the SNP matrix of GBS pipelines (TASSEL) [58]. ISMU can handle GBS/RAD data and produce the resultant matrix with PIC value and heterozygosity, another unique feature not found in other pipelines. Generally pipelines requires the user to execute a series of commands step-by-step for analysis but ISMU makes this process highly automated that the user needs to upload only the inputs and the results would be presented.

The pipeline is designed to handle genomic (WGRS or RAD) and transcriptomic datasets which include both SE (single end) and PE (paired-end) reads. To facilitate downstream analysis, SNP matrix or a matrix of called alleles for all genotypes is presented. The SNPs reported as a result were to be considered of high confidence. In WGRS dataset, the number of SNPs was observed to be very high with an average SNP density of 1.1/kb, while in RAD dataset SNP density was 0.05/kb owing to the nature of complexity reduction technologies which does not capture the complete variation. In RAD dataset, the extent of missing genotype information at the called position is huge and requires a better depth of sequencing or imputation of data. For alignment of RNAseq dataset, SOAP2 was used which neither allows gap nor clipping at the ends of reads at alignment. Because of this strict alignment mode, SOAP2 aligned lesser proportion of reads to distant reference sequences (unigene), evident in the low number of SNPs detected in comparison with gapped alignment based methods [43].


ISMU is a validated SNP mining pipeline for NGS data from single or multiple samples. The prominent features of the pipeline include GUI, parallelization, multiple analysis tools, and multi-user support to facilitate analysis of huge NGS data for genotyping experiments. Although it has been developed for use with crop plants (e.g. chickpea, pigeonpea, etc.) it could also be used with animal and microbe data. Hence, the data could be processed independent of the organism. The easy to use GUI encourages non-bioinformatics scientists/researchers to analyse the data in their laboratories on the desktop machines. ISMU integrates proven NGS analysis tools and offers multiple options for alignment and SNP calling. It includes a complete workflow from pre-processing of raw data to deciphering SNPs and reporting markers towards genotyping experiments. The pipeline outputs variation information of the samples analyzed in a simple format for downstream analyses such as genotyping studies. Parallelization of ISMU enables efficient use of modern desktops with multiple cores. The complete application is rigorously tested and is also distributed on CDs as well as ready to use virtual box image.

ISMU targets plant molecular breeding community with little computational expertise. Plant breeders/researchers could potentially use it to quickly process the data and thereby obtain biological insights into genetic events. The SNPs detected by the pipeline could be used in applications such as linkage mapping, trait mapping, TILLING, GWAS and QTLSeq (Bulk segregant analysis).

Materials and Methods

Assembling tools

In order to develop an ISMU pipeline, open source tools such as BWA v0.6 ( [41], Maq v0.7 ( [42], Bowtie2 v2.1.0 ( [43], NovoAlign v2.07.13 ( [44], and SOAP2 v2.21 ( [45] for alignment, SAMtools v0.1.19 ( and CbCC v2.0 [48] for SNP calling, Tablet v1.12.03.26 ( [51] for visualization of SNP and Flapjack v1.12.05.25 ( [52] for analysing allele distribution were downloaded. The details on development and their application of the above selected tools are provided in the above mentioned literature. Swings and AWT packages were used to develop GUI of the pipeline on Java (JDK 1.7) platform. Perl language v5.6 and bash scripting were used to add different functionality of pipeline. ISMU pipeline has been developed and tested on 64-bit Red Hat Linux based operating system (Cent OS 6) in Bash shell environment.

Sequence data sets

In order to evaluate the performance of newly developed ISMU pipeline, two genomic datasets from chickpea and one transcriptomic plant dataset from peanut were collected. The first dataset comprises of the whole genome re-sequencing (WGRS) data from four genotypes of chickpea namely Pistol, Slasher, Hat Trick and Genesis 90, as reported in Varshney et al. [59]. The second dataset was the RAD sequencing dataset from ten genotypes of chickpea namely ICCV 03107, ICC 4918, ICC 4930, ICC 4958, ICC 5270, ICCV 05530, ICC 5810, ICC 5912, ICC 6263 and ICC 8261 [59]. These datasets are available at The third dataset includes transcriptome data (RNAseq) from two peanut genotypes HuaU12 (SRR647081) and HuaU606 (SRR647076) downloaded from sequence read archive (SRA) database of NCBI. The WGRS and RNAseq datasets were paired end (PE) data while the RAD dataset was single end (SE). The WGRS and RAD datasets use the chickpea draft genome sequence [59] as a reference sequence while RNAseq dataset use peanut unigene representative sequences downloaded from the UniGene database (

Development of the pipeline

Tools for alignment (BWA, Maq, Bowtie2, NovoAlign, and SOAP2), SNP calling (Samtools and CbCC), visualization of SNPs (Tablet, Flapjack) and analysing allele distribution (Flapjack) were decompressed and compiled on Redhat Linux. These tools were then integrated into a pipeline using Perl language. In this process, several in-house perl/bash scripts were written and embedded into the pipeline in order to bring automation, functionality and visualization. These scripts were used to provide required features such as pre-processing of data, create input files for Flapjack, processing SNP for calculating allele frequency, PIC value and generate files for developing Illumina and KASPar assays. Most of the scripts were written with multi-threading feature for speedy execution of the analysis for handling large datasets. In order to develop a GUI pipeline, interactive interface was developed related to data upload, selection of tools, customizing parameters for analysis, location of analysed data and output format. The input given by user through GUI is passed to backbone script of the pipeline which automatically generates a shell script to execute the different steps of pipeline. During the execution, shell scripted calls other softwares and embedded scripts in step wise manner while a GUI window shows the status of a run. In addition, it also provide an option to terminate the execution of pipeline at any stage. At the end of execution of pipeline, another page pops up showing options to download and display different outputs. The pipeline save all the output files in the user provided output folder which can be retrieved by the user at later stage.

Assessment of the pipeline

Selected datasets were subjected to quality filtering in order to discard low quality reads i.e., reads having more than 30% of low quality bases (Q<20). These filtered reads were trimmed from both the ends which resulted in shorter read lengths. The resultant paired end reads would not be uniform in length. The reads passing these quality filters in the WGRS, RAD and RNAseq datasets were then aligned to a reference using different alignment tools in ISMU. WGRS dataset and RAD datasets were aligned to chickpea genome using BWA and Bowtie2 respectively. RNAseq dataset was mapped against unigenes of peanut using SOAP2. Samtools was used to call variants for WGRS and RAD datasets while CbCC was used with RNAseq dataset. All datasets were analysed on a 24 core Linux machine with 48 GB RAM.

Author Contributions

Conceived and designed the experiments: RKV. Performed the experiments: SA AR TS PR MT. Analyzed the data: SA. Contributed reagents/materials/analysis tools: SA BPA PR MT. Wrote the paper: SA TA MAVSKK RKV.


  1. 1. Imelfort M, Edwards D (2009) De novo sequencing of plant genomes using second-generation technologies. Brief Bioinform 10: 609–618
  2. 2. Varshney RK, Nayak SN, May GD, Jackson SA (2009) Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol 27: 522–530
  3. 3. Marshall DJ, Hayward A, Eales D, Imelfort M, Stiller J, et al. (2010) Targeted identification of genomic regions using TAGdb. Plant Methods 6: 19–19
  4. 4. Michael TP, Jackson S (2013) The first 50 plant genomes. Plant Gen 6 : doi:10.3835/plantgenome2013.03.0001
  5. 5. Duran C, Appleby N, Vardy M, Imelfort M, Edwards D, et al. (2009) Single nucleotide polymorphism discovery in barley using autoSNPdb. Plant Biotechnol J 7: 326–333
  6. 6. Lu T, Lu G, Fan D, Zhu C, Li W, et al. (2010) Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res 20: 1238–1249
  7. 7. Dubey A, Farmer A, Schlueter J, Cannon SB, Abernathy B, et al. (2011) Defining the transcriptome assembly and its use for genome dynamics and transcriptome profiling studies in pigeonpea (Cajanus cajan L.). DNA Res 18: 153–164
  8. 8. Garg R, Patel RK, Tyagi AK, Jain M (2011) De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res 18: 53–63
  9. 9. Hiremath PJ, Farmer A, Cannon SB, Woodward J, Kudapa H, et al. (2011) Large-scale transcriptome analysis in chickpea (Cicer arietinum L.), an orphan legume crop of the semi-arid tropics of Asia and Africa. Plant Biotechnol J 9: 922–931
  10. 10. Deschamps S, la Rota M, Ratashak JP, Biddle P, Thureen D, et al. (2010) Rapid genome-wide single nucleotide polymorphism discovery in soybean and rice via deep resequencing of reduced representation libraries with the Illumina genome analyzer. Plant Gen 3: 53–68
  11. 11. van Tassell CP, Smith TPL, Matukumalli LK, Taylor JF, Schnabel RD, et al. (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5: 247–252
  12. 12. Gore MA, Chia J-M, Elshire RJ, Sun Q, Ersoz ES, et al. (2009) A first-generation haplotype map of maize. Science 326: 1115–1117
  13. 13. Hyten DL, Cannon SB, Song Q, Weeks N, Fickus EW, et al. (2010) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics 11: 38–38
  14. 14. Wu X, Ren C, Joshi T, Vuong T, Xu D, et al. (2010) SNP discovery by high-throughput sequencing in soybean. BMC Genomics 11: 469–469
  15. 15. van Orsouw NJ, Hogers RCJ, Janssen A, Yalcin F, Snoeijers S, et al. (2007) Complexity reduction of polymorphic sequences (CRoPS): a novel approach for large-scale polymorphism discovery in complex genomes. PLoS One 2: e1172
  16. 16. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, et al. (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3: e3376
  17. 17. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, et al. (2011) A robust, Simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLoS One 6: e19379
  18. 18. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, et al. (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 2: 499–510
  19. 19. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, et al. (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19: 1068–1076
  20. 20. Metzker ML (2010) Sequencing technologies - the next generation. Nat Rev Genet 11: 31–46
  21. 21. Kilian B, Graner A (2012) NGS technologies for analyzing germplasm diversity in genebanks. Brief Funct Genomics 11: 38–50
  22. 22. You FM, Huo N, Deal KR, Gu YQ, Luo M-C, et al. (2011) Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence. BMC Genomics 12: 59–59
  23. 23. Gaur R, Azam S, Jeena G, Khan AW, Choudhary S, et al. (2012) High-throughput SNP discovery and genotyping for constructing a saturated linkage map of chickpea (Cicer arietinum L.). DNA Res 19: 357–373
  24. 24. Hufford MB, Xu X, van Heerwaarden J, Pyhajarvi T, Chia J-M, et al. (2012) Comparative population genomics of maize domestication and improvement. Nat Genet 44: 808–811
  25. 25. Xu X, Liu X, Ge S, Jensen JD, Hu F, et al. (2012) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat Biotechnol 30: 105–111
  26. 26. Lam H-M, Xu X, Liu X, Chen W, Yang G, et al. (2010) Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet 42: 1053–1059
  27. 27. Hiremath PJ, Kumar A, Penmetsa RV, Farmer A, Schlueter JA, et al. (2012) Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnol J 10: 716–732
  28. 28. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, et al. (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic gapping to compare with the B73 reference genome. PLoS ONE 6: e28334
  29. 29. Saxena RK, Penmetsa RV, Upadhyaya HD, Kumar A, Carrasquilla-Garcia N, et al. (2009) Large-scale development of cost-effective single-nucleotide polymorphism marker assays for genetic mapping in pigeon pea and comparative mapping in legumes. DNA Res 19: 449–461
  30. 30. Yu H, Xie W, Li J, Zhou F, Zhang Q (2014) A whole-genome SNP array (RICE6K) for genomic breeding in rice. Plant Biotechnol J 12: 28–37
  31. 31. Akhunov E, Nicolet C, Dvorak J (2009) Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina GoldenGate assay. Theor Appl Genet 119: 507–517
  32. 32. Gupta PK, Rustgi S, Mir RR (2008) Array-based high-throughput DNA markers for crop improvement. Heredity 101: 5–18
  33. 33. Ganal MW, Polley A, Graner E-M, Plieske J, Wieseke R, et al. (2012) Large SNP arrays for genotyping in crop plants. J Biosci 37: 821–828
  34. 34. Blanca JM, Pascual L, Ziarsolo P, Nuez F, Cañizares J (2011) ngs backbone: a pipeline for read cleaning, mapping and snp calling using next generation sequence. BMC Genomics 12: 285
  35. 35. Deng X (2011) Seqgene: a comprehensive software solution for mining exome- and transcriptome sequencing data. BMC Bioinformatics 12: 267
  36. 36. Sana ME, Iascone M, Marchetti D, Palatini J, Galasso M, et al. (2011) Games identifies and annotates mutations in next-generation sequencing projects. Bioinformatics 27: 9–13
  37. 37. Qi J, Zhao F, Buboltz A, Schuster SC (2010) ingap: an integrated next-generation genome analysis pipeline. Bioinformatics 26: 127–129
  38. 38. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, et al. (2011) A framework for variation discovery and genotyping using next-generation dna sequencing data. Nat Genet 43: 491–498
  39. 39. Cock PJA, Fields CJ, Goto N, Heuer ML, Rice PM (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38: 1767–1771
  40. 40. Fastq format. Available: Accessed 2013 Oct.
  41. 41. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589–595
  42. 42. Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18: 1851–1858
  43. 43. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9: 357–359
  44. 44. Novocraft short read alignment package. Available: Accessed 2013 Oct.
  45. 45. Li R, Yu C, Li Y, Lam T-W, Yiu S-M (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25: 1966–1967
  46. 46. SOAP: Short oligonucleotide analysis package. Available: Accessed 2013 Oct.
  47. 47. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, et al. (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25: 2078–2079
  48. 48. Azam S, Thakur V, Ruperao P, Shah T, Balaji J, et al. (2012) Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome. Am J Bot 99: 186–192
  49. 49. Genomes. Available: Accessed 2013 Oct.
  50. 50. Li R, Li Y, Fang X, Yang H, Wang J, et al. (2009) SNP detection for massively parallel whole-genome resequencing. Genome Res 19: 1124–1132
  51. 51. Milne I, Bayer M, Cardle L, Shaw P, Stephen G (2010) Tablet—next generation sequence assembly visualization. Bioinformatics 26: 401–402
  52. 52. Milne I, Shaw P, Stephen G, Bayer M, Cardle L, et al. (2010) Flapjack—graphical genotype visualization. Bioinformatics 26: 3133–3134
  53. 53. Generic feature format version 3 (GFF3). Available: Accessed 2013 Oct.
  54. 54. Nielsen R, Paul JS, Albrechtsen A, Song YS (2011) Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 12: 443–451
  55. 55. Assay design tool (ADT). Available: Accessed 2014 Oct.
  56. 56. Flicek P, Birney E (2009) Sense from sequence reads: methods for alignment and assembly. Nat Methods 6: 6–6
  57. 57. Li H, Homer N (2010) A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform 11: 473–483
  58. 58. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, et al. (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23: 2633–2635
  59. 59. Varshney RK, Song C, Saxena RK, Azam S, Yu S, et al. (2013) Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol 31: 240–246
  60. 60. Fischer M, Snajder R, Pabinger S, Dander A, Schossig A, et al. (2012) SIMPLEX: cloud-enabled pipeline for the comprehensive analysis of exome sequencing data. PLoS ONE 7: e41948