Address of the bookmark: http://genomearchitect.github.io/

The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.

Address of the bookmark: http://github.com/hendrixlab/bpRNA

NucDiff provides information about the types of differences and their locations. It is possible to upload the results into genome browser for visualization and further inspection. It was written in Python and uses the NUCmer package from MUMmer[1] for sequence comparison.

Address of the bookmark: https://github.com/uio-cels/NucDiff

Address of the bookmark: https://github.com/tanghaibao/jcvi

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

Address of the bookmark: https://github.com/lh3/miniasm

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

1. Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.
2. Reducing/removing reference bias as compared to mapping based approaches.

The software is designed to work in situations where a whole-genome assembly is not the objective, but rather when the researcher wishes to assemble discreet 'targets' contained within next-generation shotgun sequence data. ARC decomplexifies the traditionally difficult problem of assembly by breaking the reads into small, manageable subsets which can then be assembled quickly and efficiently in parallel. Applications include those in which the researcher wishes to de novo assemble specific content and a set of semi-similar reference targets is available to initialize the assembly process.

https://ibest.github.io/ARC/

Address of the bookmark: https://ibest.github.io/ARC/

Address of the bookmark: https://github.com/ruanjue/wtdbg2

Address of the bookmark: https://github.com/oushujun/EDTA

Please see Achieving Success with De Novo Assembly and System Requirements before creating your Chromium libraries for assembly.

Supernova should be run using 38-56x coverage of the genome.
• Somewhat higher coverage is sometimes advantageous.
• Supernova will exit if it finds that coverage is far from the recommended range.
• Note that at most 2.14 billion reads are allowed.
• Please note that we have not extensively tested genomes larger than human, and any genome above approximately 4 GB should be considered experimental and is not supported.

Address of the bookmark: https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running