Recovering genes from targeted sequence capture data
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examples
test_dataset
LICENSE.txt
README.md
citation_list.txt
cleanup.py
depth_calculator.py
distribute_reads_to_targets.py
distribute_reads_to_targets_bwa.py
distribute_targets.py
exonerate_hits.py
fasta_merge.py
gene_recovery_heatmap.R
gene_recovery_heatmap_ggplot.R
get_seq_lengths.py
hybpiper_stats.py
hybseq_summary.py
intronerate.py
paralog_investigator.py
paralog_retriever.py
query_file_builder.py
reads_first.py
retrieve_sequences.py
spades_runner.py

README.md

HybPiper

Current version: 1.2 (May 2017)

Join the chat at https://gitter.im/mossmatters/HybPiper DOI

-- Read our article in Applications in Plant Sciences (Open Access)

by Matt Johnson and Norm Wickett, Chicago Botanic Garden

(logo by Elliot Gardner)

Purpose

HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.

Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.

HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information, please see our wiki.

HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.


Dependencies

  • Python 2.7 or later (to use the argparse module for help documents)
  • BIOPYTHON 1.59 or later (For parsing and handling FASTA and FASTQ files)
  • EXONERATE (For aligning recovered sequences to target proteins)
  • BLAST command line tools (Aligning reads to target protiens)
  • SPAdes (Assembling reads into contigs)
  • GNU Parallel (Handles parallelization of searching, assembling, and aligning)

Required for BWA version of the pipeline and for the intron and depth calculation scripts:

NOTE: A previous version of the pipeline required Velvet and CAP3 for assembly. These have been unreliable at assembling individual genes, and SPAdes has replaced them.


Setup

We have successfully installed HybPiper on MacOSX and Linux (Centos 6). All of the bioinformatics tools can be installed with homebrew or linuxbrew.

For full installation instructions, please see our wiki page:

https://github.com/mossmatters/HybPiper/wiki/Installation

Once all dependencies are installed, execute the run_tests.sh script from the test_dataset directory for a demonstration of HybPiper.


Pipeline Input

Full instructions on running the pipeline, including a step-by-step tutorial using a small test dataset, is available on our wiki:

https://github.com/mossmatters/HybPiper/wiki

High-Throughput DNA Sequencing Reads

Before running the pipeline, you will need "cleaned" FASTQ file(s)-- one or two depending on whether your sequencing was single or paired-end. Reads should have adapter sequences removed and should be trimmed to remove low quality base calls.

Target Sequences

You will also need to construct a "target" file of gene regions. The target file should contain one gene region per sequence, with exons "concatenated" into a contiguous sequence. For more information on constructing the target file, see the wiki, or view the example file in: test_dataset/test_targets.fasta

There can be more than one "source sequence" for each gene in the target file. This can be useful if the target enrichment baits were designed from multiple sources-- for example a transcriptome in the focal taxon and a distantly related reference genome.


Pipeline Output

HybPiper will map the reads to the target sequences, sort the reads by gene, assemble the reads for each gene separately, align the contigs to the target sequence, and extract a coding sequence from each gene. Output from each of these phases is saved in a standardized directory hierarchy, making it easy for post-processing scripts to summarize information across many samples.

For example, the coding sequence for gene "gene001" for sample "EG30" is saved in a FASTA file:

EG30/gene001/EG30/sequences/FNA/gene001.FNA

and a list of genes for which a sequence could be extracted can be found:

EG30/genes_with_seqs.txt

For a full description of HybPiper output, see the wiki.


Changelog

1.3 The Herbarium Update January, 2018

Features

  • Added --exclude flag to be the inverse of --target: all sequences with the specified string will not be used as targets for exon extraction (they will still be used for read-mapping). Useful if you want to add supercontig sequence to the target file, but not use it for exon extraction.

  • Added --addN to intronerate.py. This feature will add 10 N characters in between joined contig when recovering the supercontig. This is useful for identifying where the intron recovery fails, and for annotation processing (i.e. for GenBank).

  • Added a new version of the heatmap script, gene_recovery_heatmap_ggplot.R. This script is much simpler and produces nice color PNG images, but struggles a bit on PDF output. The original heatmap script is stil included. Thanks to Paul Wolf for the ggplot code!

Bug Fixes

  • Fixed misassembly of supercontigs when there are multiple alignments to different parts of the same exon.
  • Fixed poor filtering of GFF results to produce intron/exon annotation.
  • Fixed non-propogation of exonerate parameters

1.2.1 September, 2017

Bug Fixes

  • Fixed assembly issue when gene does not have unpaired reads.
  • Fixed distribution of targets when unpaired reads present.
  • Fixed use of unpaired reads to detect best target.

1.2 May, 2017

Features

  • Added --unpaired flag. When using paired-end sequencing reads, a third read file may be specified with this flag. Reads will be mapped to targets separately, but will be used along with paired reads in contig assembly.

  • Added --target flag. Adds the ability to choose which of the reference sequences is used for each gene. If --target is a file (tab-delimited file with one gene and one target name per line), HybPiper will use that. Otherwise --target can be the name of one reference. HybPiper will only use targets with the specified name in the Alignment/Exon Extraction phase. All other targets for that locus will only be used in the Mapping/Read Sorting phase.

  • Added --timeout flag, which uses GNU Parallel to kill processes (i.e. Spades or Exonerate) if they take X percent longer than average. Use if there are a lot of stuck jobs (--timeout 1000)

  • Python 3 compatibility

Bug Fixes

  • Can accommodate Solexa FASTQ paired headers
  • Fixed spades_runner.py not recognizing --cpu on redos
  • Prints more meaningful messages for some common errors
  • Can accommodate prefix not being in current directory
  • Deletes sorted reads on restart to prevent double counting reads.
  • spades_runner.py will now respect --kvals
  • Added initial call to log for reads_first.py

1.1 May, 2016: Release associated with manuscript in Applications in Plant Sciences.

  • Added paralog_investigator.py, which detects and extracts long exons from putative paralogs in all genes in one sample.

  • Added paralog_retriever.py, which retrieves sequences generated by paralog_investigator.py for many samples (or the coding sequence generated by exonerate_hits.py if no paralogs are detected).

  • Added a test_dataset of 13 genes for 9 samples, and a shell script to run the test data through the main script and several post-processing scripts.

  • Fixed bug involving calling HybPiper with a relative path such as: ../reads_first.py

  • reads_first.py --check_depend now checks for SPAdes, BWA, and Samtools

  • Full revision of README, which is now shorter. Full tutorials on installing and running HybPiper are now on the Wiki.

1.0 Feb, 2016: Initial fully-featured release associated with submission of manuscript to Applications in Plant Sciences.

-- Sequence assembly now uses SPAdes rather than Velvet + CAP3


Citation

Johnson, M. G., Gardner, E. M., Liu, Y., Medina, R., Goffinet, B., Shaw, A. J., Zerega, N. J. C, and Wickett, N. J. (2016). HybPiper: Extracting Coding Sequence and Introns for Phylogenetics from High-Throughput Sequencing Reads Using Target Enrichment. Applications in Plant Sciences, 4(7), 1600016. doi:10.3732/apps.1600016