The Shenzhen company says the low cost will be possible with an “extreme” DNA sequencing system it plans to offer that is capable of decoding the genomes of 100,000 people a year.

Ref: https://www.technologyreview.com/s/615289/china-bgi-100-dollar-genome/

http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf

Address of the bookmark: https://www.broadinstitute.org/viral-genomics/vicuna

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

Address of the bookmark: https://bioinformatics.um6p.ma/AlignStatPlot/

• paired-end (PE) and/or mate-pair libraries (NGS-based mode)
• long reads (NGS-based mode)
• synteny to the genome of some related species (reference-based mode)

Scaffolding 

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

• matches not satisfying cut-offs (--identity and --overlap)
• suboptimal matches (only best match of each query to reference is kept)
• and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

• Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
• pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
• pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
• pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
• Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

More at http://biorxiv.org/content/early/2016/09/21/076463 

Address of the bookmark: http://shinyheatmap.com/

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you. 

Address of the bookmark: http://revigo.irb.hr/

Address of the bookmark: https://github.com/gaitat/GenomeUPlot

Install

pip install genomenotebook

How to use

Create a simple genome browser with a search bar. The sequence appears when zooming in.

import genomenotebook as gn

g=gn.GenomeBrowser(genome_path, gff_path, init_pos=10000)
g.show()

Tracks can be added to visualize your favorite genomics data. See Examples for more !!!!

Address of the bookmark: https://dbikard.github.io/genomenotebook/