github.com - SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li....
www.broadinstitute.org - DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or...
www.nature.com - Validated a widely accessible approach that can be used to establish functional causality for noncoding sequence variants identified by GWASs.
https://www.nature.com/articles/nm.3975
github.com - MUM&Co is able to detect:Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)Inversions (>=1kb) and translocations (>=10kb)
talks.biogo.googlecode.com - Another good lecture for Illumina sequencing data analysis from
Dan Kortschak, Bioinformatics Group, School of Molecular and Biomedical Science ,The University of Adelaide
github.com - PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.
More at https://github.com/neufeld/pandaseq
neufeldserver.uwaterloo.ca - PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable...
github.com - BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.
The BFC algorithm is a...
github.com - medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods...