Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi

Full documentation on Read the Docs.

A set of tools for viral metagenomics.

virmet is called with a command subcommand syntax: virmet fetch --viral n, for example, downloads the bacterial database. Other available subcommands so far are

• fetch download genomes
• update update viral/bacterial database
• index index genomes
• wolfpack analyze a Miseq run
• covplot plot coverage for a specific organism

A short help is obtained with virmet subcommand -h.

More at https://github.com/ozagordi/VirMet

Address of the bookmark: https://github.com/ozagordi/VirMet

Address of the bookmark: https://github.com/envgen

This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full

Address of the bookmark: http://www.isical.ac.in/~bioinfo_miu/FOGSAA.htm

Address of the bookmark: https://github.com/fenderglass/Flye

The MMseqs2 user guide is available as Github Wiki or as PDF file (Thanks to pandoc!)

Please cite: Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

Address of the bookmark: https://github.com/soedinglab/MMseqs2

Features

• Very accurate results (Martin et al., WhatsHap: fast and accurate read-based phasing)
• Works well with Illumina, PacBio, Oxford Nanopore and other types of reads
• It phases SNVs, indels and even “complex” variants (such as TCG → AGAA)
• Pedigree phasing mode uses reads from related individuals (such as trios) to improve results and to reduce coverage requirements (Garg et al., Read-Based Phasing of Related Individuals).
• WhatsHap is easy to install
• It is easy to use: Pass in a VCF and one or more BAM files, get out a phased VCF. Supports multi-sample VCFs.
• It produces standard-compliant VCF output by default
• If desired, get output that is compatible with ReadBackedPhasing
• Open Source (MIT license)

Address of the bookmark: https://whatshap.readthedocs.io/en/latest/

Address of the bookmark: https://github.com/brentp/goleft

per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.
mean per-window depth given a window size--as would be used for CNV calling.
the mean per-region given a BED file of regions.
a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.
quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)
threshold output to indicate how many bases in each region are covered at the given thresholds.
A summary of mean depths per chromosome and within specified regions per chromosome.

Address of the bookmark: https://github.com/brentp/mosdepth