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.

Address of the bookmark: https://github.com/mossmatters/HybPiper

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

1. Prepare a reference sequence (ref.fasta). Better a bacterial genome.
2. Download maq, maq-data and maqview at the download page.
3. Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
4. Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:

   maq.pl demo ref.fasta calib-30.dat
   

   where calib-30.dat is contained in maq-data.
5. View the alignment:

   cd maqdemo/easyrun;
   maqindex -i -c consensus.cns all.map;
   maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

Address of the bookmark: http://maq.sourceforge.net

Address of the bookmark: https://github.com/alastair-droop/fqtools

EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z

Address of the bookmark: https://github.com/apeltzer/EAGER-GUI

More at http://chibba.agtec.uga.edu/duplication/mcscan/

Address of the bookmark: http://chibba.agtec.uga.edu/duplication/mcscan/

Address of the bookmark: http://www.mgc.ac.cn/GenomeComp/

Know of any other open source software for PacBio data? Email us.

Software categories:

• De novo assembly
• Structural Variations Detection
• Reference-based alignment
• Consensus and variant calling
• RNA analysis
• Epigenetic base modifications and methylation
• Barcoding
• Genome Browsers
• Run QC
• Frameworks and APIs

Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data

The work in our lab has focussed on computational approaches to speed-up the finishing process. Specifically, we have explored the use of optical mapping and mate-pair data to augment assemblies and direct finishing experiments. The tools developed in our lab have been used in several finishing projects, producing complete genomes (and near-complete ones) with surprisingly little computational and experimental effort (Nagarajan et al., in submission). The executables (as well as source code) for these tools are freely available here:

• Scaffolding using Optical Restriction Mapping
  Optical Maps are global, ordered maps of restriction site locations in a genome. This information can be quite useful in scaffolding contigs from a shotgun assembly to guide the finishing process. A set of programs to exploit optical maps for assembly can be found here: SOMA v2.0 (63 MB tar.gz file). This version of SOMA contains several improvements to programs in v1.0 as well as new scripts for working with multiple maps, contig graphs and scaffolds. 
  
  
• Augmenting assemblies with mate-pair data
  Mate-pair information can be valuable in augmenting short-read assemblies and reconstructing the genome as larger scaffolds. AMOS-Hybrid is a pipeline written in the AMOS framework (open-source assembly tools) to merge arbitrary mated reads into an existing assembly and merge contigs and create scaffolds where possible. Source code and executables for AMOS-Hybrid are available here: AMOS-Hybrid v1.0 (142 MB tar.gz file). 
  
  
• Assembly and sequence-composition guided finishing
  Contigs from a shotgun assembly are typically linked together in a graph structure that can serve to guide finishing and in some case close gaps in-silico. Also, in many cases, sequence composition of contigs can provide clues to fill gaps in scaffolds. A set of scripts to automate some of these tasks can be found here: Finishing Scripts v1.0 (63 MB tar.gz file). 

http://www.cbcb.umd.edu/finishing/

Address of the bookmark: http://www.cbcb.umd.edu/finishing/

http://gmod.org/wiki/Diya

Address of the bookmark: https://sourceforge.net/projects/diyg/