is a software package for visualising and analysing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualisation, demographic trajectory reconstruction, conditional posterior distribution summary and more. Tracer v1.7.1 can read output files from MrBayes, BEAST, BEAST2, RevBayes, Migrate, LAMARC and and possibly other MCMC programs from other domains..

is a compact file format for efficiently storing and querying whole-genome genotypes of tens to hundreds of thousands of samples. It can be considered as an alternative to genotype-only BCFv2. BGT is more compact in size, more efficient to process, and more flexible on query.

allows you to quickly annotate your VCF with any number of INFO fields from any number of VCFs or BED files. It uses a simple conf file to allow the user to specify the source annotation files and fields and how they will be added to the info of the query VCF.

is a user-friendly workflow for phylogenomics intended to give more researchers the capability to create phylogenomic trees.

is a variant caller for single cell data from whole genome amplification with multiple displacement amplification (MDA). It relies on a pair of samples, where one is from an MDA single cell and the other from a bulk sample of the same cell population, sequenced with any next-generation sequencing technology.

is a tool to extract paired reads in FASTQ format from coordinate sorted BAM files.

Bazam is a smarter way to realign reads from one genome to another. If you've tried to use Picard SAMtoFASTQ or samtools bam2fq before and ended up unsatisfied with complicated, long running inefficient pipelines, bazam might be what you wanted. Bazam will output FASTQ in a form that can stream directly into common aligners such as BWA or Bowtie2, so that you can quickly and easily realign reads without extraction to any intermediate format. Bazam can target a specific region of the genome, specified as a region or a gene name if you prefer.

implements a method designed to map raw reads directly against redundant databases, in an ultra-fast manner using seed and extend. KMA is particulary good at aligning high quality reads against highly redundant databases, where unique matches often does not exist. It works for long low quality reads as well, such as those from Nanopore. Non-unique matches are resolved using the "ConClave" sorting scheme, and a consensus sequence are outputtet in addition to other common attributes, based on our users demands.