Banana Slug Genomics

BLESS

SGA

Musket

RACER

Skewer (Jiang et al. 2014)

AdapterRemoval (Lindgreen 2012)

Trimmomatic (Bolger et al. 2014)

Blast

Allpaths-LG

Most assemblers use kmers to create a de Bruijn graph

In graph theory, an n-dimensional De Bruijn graph of m symbols is a directed graph representing overlaps between sequences of symbols. It has mn vertices, consisting of all possible length-n sequences of the given symbols; the same symbol may appear multiple times in a sequence” - http://en.wikipedia.org/wiki/De_Bruijn_graph

de Bruijn Graph:

SSAKE

Overlap, Layout, Consensus

Allpaths-LG is only for long read data

Platanus was made for heterozygous genomes

SOAP, Allpaths-LG will help you find proper kmer size

Abyss has tools to assess quality (abyss-fac)

Once you run the assembly, you can get rid of almost everything except the contigs and scaffolds (unitigs is the genome w/o any gap regions)

N50 at least 50% of the bases are in scaffolds this size or bigger

A contig N50 is calculated by first ordering every contig by length from longest to shortest. Next, starting from the longest contig, lenghts of each contig are summed, until this running sum equals one-half o the total length of all contigs in the assemly. The contig N50 of the assembly is the length of the shortest contig in this list (Yandel and Ence 2012).

Not always a good indicator - will not detect misassemblies

How do you know which kmer size is better? Some people just use N50

CEGMA (recommended) searches for genes that should be found in all eukaryotes in the final assembly.

Using RNA transcripts: Baa.pl (Ryan 2013/4, Arxiv)

De novo liklihood-based measures (LAP; Ghodsi et al. 2013)

Feature Response Curve

GapCloser (Luo et al. 2012)

GapFiller (Nadalin et al. 2011)

Tries to extend alignments (not done by assemblers)

Can run multiple times, but first cycle is the most effective

Complete genes (CEGMA) much better after gap closing

Works better on some assemblies than others

REAPR (Hunt et al. 2013) better than NxRepair

NxRepair (Murphy et al. 2014)

Breaks down scaffolds without enough evidence

Run REAPR on the best assembly candidate

Need at least one paired library

Looks for areas with low coverage

Compares expected coverage to actual coverage

Really low coverage is a misassembly

Will remove those areas if you let it, but those are often areas that have been gap filled

Run CEGMA before and after, usually score doesn’t change (finding real misassemblies)

ALWAYS:

Metassembler (Wences and Schatz 2014, Arxiv)

GAM-NGS (Vicedomini et al. 2013)

Allpaths/abyss/soap → gap closer → REAPR → gap closer → final

Read clipping marks the quality of the read. Soft masking (low quality gets lowercase character) and hard masking (low quality gets replaced by N character)

Assemblies are never finished - still over 100 gaps in human genome

What is a good genome?

Repeat annotation

Gene annotation

Mapping to get Diploid Genome (most assemblers won't call polymorphisms)