NGS-Pipe provides analyses for large scale DNA and RNA sequencing experiments. The scope of pre-implemented functions spans the detection of germline variants, somatic single nucleotide variants (SNV) and insertion and deletion (InDel) identification, copy number event detection, and differential expression analyses. Further, it provides pre-configured workflows, such that the final mutational information as well as quality reports and all intermediate results can be generated quickly, also by inexperienced users. In addition, the pipeline can be used on a single computer or in a cluster environment where independent steps are executed in parallel. If one of the steps of the pipeline fails and produces incomplete or no results, the computation of all depending steps is halted and an error message is shown. However, after the issue is resolved the pipeline independently resumes the analyses at the appropriate point, eliminating the need to rerun the complete analysis or manual deletion of erroneous files.
We have implemented and tested predefined workflows for the automated analysis of WES, WGS, and RNA-seq data (Fig. 1).
The primary data analysis steps include Trimmomatic (Bolger, 2014) to process raw files, BWA (Li, 2009) or STAR (Dobin, 2013) to align reads, and Picard tools (http://broadinstitute.github.io/picard), SAMtools (Li, 2009Samtools) and GATK (McKenna, 2010) to process the aligned reads.
Detecting genomic variants is highly dependent on properties of the input data, such as variant frequency, coverage, or contamination (Cai, 2016; Hofmann, 2017). For this reason, we included several variant callers in NGS-pipe, viz. Mutect (Cibulskis, 2013), JointSNVMix2 (Roth, 2012), VarScan2 (Koboldt, 2012), VarDict (Lai, 2016), SomaticSniper (Larson, 2011), Strelka (Saunder, 2012), and deepSNV (Gerstung, 2012). Further, we included SomaticSeq (Fang, 2015), which combines the results of multiple variant callers and ranked high in the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (Ewing, 2015), and the rank aggregation scheme introduced in (Hofmann, 2017).
Copy number events are detected by FACETS (Shen, 2016), or BIC-seq2 (Xi, 2016), which has been designed specifically for whole genome data.
The results of the experiments can be annotated and manipulated using SnpEff (Cingolani 2012), SnpSift (Cingolani, 2012) and ANNOVAR (Wang, 2010).
RNA-seq data is analyzed to quantify gene expression levels. We include quality control, alignment, and gene counting using the SubRead (Liao, 2014) package. Output files are reformatted to serve as direct input to tools that perform differential gene expression analysis.
The directory examples/wes/ contains a ready to go example for the analysis of three leukemia patients (Cifola, 2015). This example downloads tumor-control matched exome data sets from the Sequence Read Archive, installs the required programs, downloads the necessary reference files and builds the essentials indices. Afterwards, an analysis starting with the mapping of the reads via BWA (Li 2009) all the way to the somatic variant calling with VarScan2 (Koboldt 2012). After the installation of all tools via conda you can proceed like:
#1. Go to examples folder:
cd examples/dna
#2. Download test data: We provide an additional snakemake pipeline to
# download test sequences, databases and adapter files:
./run_prepare_data_locally.sh
# This will download 6 test data sets, the adapters, regions file,
# the human reference and build the BWA database index
#3. Execute the DNA Pipeline:
./run_analysis_locally.sh
# This will execute: RAW --> QC(Trimmomatic) --> Mapping(BWA) --> Sort(Picard)
# --> Merge(Picard) --> Remove Secondary Alignments(Samtools) --> MarkDuplicates(Picard)
# --> RemoveDuplicates(Samtools) --> SNV Calling (VarScan2)An example for RNA-seq data analysis can be found in examples/rna/ and here.
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(4), 2114-2120.
Cai, L., Yuan, W., Zhou Zhang, L. H., & Chou, K. C. (2016). In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data. Scientific reports, 6.
Cibulskis, K., Lawrence, M. S., Carter, S. L., Sivachenko, A., Jaffe, D., Sougnez, C., ... & Getz, G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology, 31(3), 213-219. ISO 690
Cifola, I., Lionetti, M., Pinatel, E., Todoerti, K., Mangano, E., Pietrelli. A. et al. (2015). Whole-exome sequencing of primary plasma cell leukemia discloses heterogeneous mutational patterns. Oncotarget, 6(19), 17543-17558.
Cingolani, P., Patel, V. M., Coon, M., Nguyen, T., Land, S. J., Ruden, D. M., & Lu, X. (2012). Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Toxicogenomics in non-mammalian species, 3, 35.
Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., ... & Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly, 6(2), 80-92.
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., ... & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21.
Ewing, A. D., Houlahan, K. E., Hu, Y., Ellrott, K., Caloian, C., Yamaguchi, T. N., ... & Calling, I. T. D. S. M. (2015). Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nature methods, 12(7), 623-630.
Fang, L. T., Afshar, P. T., Chhibber, A., Mohiyuddin, M., Fan, Y., Mu, J. C., ... & Koboldt, D. C. (2015). An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome biology, 16(1), 197.
Gerstung, M., Beisel, C., Rechsteiner, M., Wild, P., Schraml, P., Moch, H., & Beerenwinkel, N. (2012). Reliable detection of subclonal single-nucleotide variants in tumour cell populations. Nature communications, 3, 811.
Hofmann, A. L., Behr, J., Singer, J., Kuipers, J., Beisel, C., Schraml, P., ... & Beerenwinkel, N. (2017). Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics, 18(1), 8.
Koboldt, D., Zhang, Q., Larson, D., Shen, D., McLellan, M., Lin, L., Miller, C., Mardis, E., Ding, L., & Wilson, R. (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3), 568--576.
Koster, J. and Rahmann, S. (2012). Snakemake–a scalable bioinformatics workflow engine. Bioinformatics, 28(19), 2520–2522.
Lai, Z., Markovets, A., Ahdesmaki, M., Chapman, B., Hofmann, O., McEwen, R., ... & Dry, J. R. (2016). VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic acids research, 44(11), e108-e108.
Larson, D. E., Harris, C. C., Chen, K., Koboldt, D. C., Abbott, T. E., Dooling, D. J., ... & Ding, L. (2011). SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics, 28(3), 311-317.
Li H. and Durbin R. (2009). Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics, 25(14), 1754-1760.
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., ... & Durbin, R. (2009). The sequence alignment/map format and SAMtools. Bioinformatics, 25(16), 2078-2079.
Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923-930.
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research, 20(9), 1297-1303.
Roth, A., Ding, J., Morin, R., Crisan, A., Ha, G., Giuliany, R., ... & Marra, M. A. (2012). JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics, 28(7), 907-913.
Saunders, C. T., Wong, W. S., Swamy, S., Becq, J., Murray, L. J., & Cheetham, R. K. (2012). Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics, 28(14), 1811-1817.
Shen, R., & Seshan, V. E. (2016). FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic acids research, 44(16), e131-e131.
Wang, K., Li, M., & Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research, 38(16), e164-e164.
Xi, R., Lee, S., Xia, Y., Kim, T. M., & Park, P. J. (2016). Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants. Nucleic acids research, gkw491.