BOL: Related items

TEannot

Jit — Thu, 18 Aug 2016 10:02:03 -0500

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

GeneMANIA

Jit — Mon, 22 Aug 2016 09:55:16 -0500

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Address of the bookmark: http://genemania.org/

MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads

Rahul Nayak — Fri, 11 May 2018 05:07:45 -0500

MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current PBcR-Mhap pipeline. MECAT performance were compared with PBcR-Mhap pipeline, FALCON and Canu(v1.3) in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the PBcR-Mhap pipeline and FALCON.

https://www.nature.com/articles/nmeth.4432

Address of the bookmark: https://github.com/xiaochuanle/MECAT

GeneValidator - Identify problems with predicted genes

Poonam Mahapatra — Fri, 26 Aug 2016 06:00:03 -0500

GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.

If you would like to use GeneValidator on a few sequences, see our online GeneValidator Web App -http://genevalidator.sbcs.qmul.ac.uk.

If you use GeneValidator in your work, please cite us as follows:

Dragan M‡, Moghul MI‡, Priyam A, Bustos C & Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics, doi: 10.1093/bioinformatics/btw015.

Address of the bookmark: https://github.com/wurmlab/genevalidator

Cerulean: A hybrid assembly using high throughput short and long reads

Rahul Nayak — Tue, 05 Jun 2018 10:10:15 -0500

Cerulean extends contigs assembled using short read datasets like Illumina paired-end reads using long reads like PacBio RS long reads. Cerulean v0.1 has been implemented with bacterial genomes in mind. The method is fully described in Deshpande, V., Fung, E. D., Pham, S., & Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933. http://arxiv.org/abs/1307.7933

Address of the bookmark: https://sourceforge.net/projects/ceruleanassembler/

R-chie

Jit — Thu, 01 Sep 2016 11:47:24 -0500

R-chie allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information.
R4RNA is the R package powering R-chie, available for download and local use for more customized figures and scripting.

http://www.e-rna.org/r-chie/plot.cgi?eg=single

Address of the bookmark: http://www.e-rna.org/r-chie/plot.cgi?eg=single

FinisherSC:a repeat-aware tool for upgrading de novo assembly using long reads

Jit — Mon, 20 Aug 2018 04:08:50 -0500

Here is the command to run the tool:

python finisherSC.py destinedFolder mummerPath

If you are running on server computer and would like to use multiple threads, then the following commands can generate 20 threads to run FinisherSC.

python finisherSC.py -par 20 destinedFolder mummerPath

Sometimes, if the names of raw reads and contigs consists of special characters/formats, FinisherSC/MUMmer may not parse them correctly. In that case, you want to have a quick renaming of the names of contigs/reads in contigs.fasta or raw_reads.fasta using the following command.

    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' raw_reads.fasta > newRaw_reads.fasta
    cp newRaw_reads.fasta raw_reads.fasta
    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' contigs.fasta > newContigs.fasta
    cp newContigs.fasta contigs.fasta

Address of the bookmark: https://github.com/kakitone/finishingTool

Assembly tutorial PPT

Jit — Wed, 07 Sep 2016 03:12:53 -0500

Saved Cornell University assembly workshop PPT.

Reference:

http://cbsu.tc.cornell.edu/lab/doc/assembly_workshop_20150420_lecture1.pdf

LR_Gapcloser: a tiling path-based gap closer that uses long reads to complete genome assembly

Rahul Nayak — Thu, 14 May 2020 15:09:52 -0500

LR_Gapcloser is a gap closing tool using long reads from studied species. The long reads could be downloaed from public read archive database (for instance, NCBI SRA database ) or be your own data. Then they are fragmented and aligned to scaffolds using BWA mem algorithm in BWA package. In the package, we provided a compiled bwa, so the user needn't to install bwa. LR_Gapcloser uses the alignments to find the bridging that cross the gap, and then fills the long read original sequence into the genomic gaps.

Address of the bookmark: https://github.com/CAFS-bioinformatics/LR_Gapcloser

FERMI

Jit — Fri, 09 Sep 2016 05:37:13 -0500

Fermi is a de novo assembler with a particular focus on assembling Illumina short sequence reads from a mammal-sized genome. In addition to the role of a typical assembler, fermi also aims to preserve heterozygotes which are often collapsed by other assemblers. Its ultimate goal is to find a minimal set of
unitigs to represent all the information in raw reads.

Fermi follows the overlap-layout-consensus paradigm and uses the FM-DNA-index (FMD-index) as the key data structure. It is inspired by the string graph assembler (Simpson and Durbin, 2010 and 2012) and has a similar workflow.

As a typical de novo assembler, fermi tends to produce contigs with slightly longer N50. However, the major weakness of fermi is the high misassembly rate. Although fermi provides a tool to fix misassemblies by using paired-end reads to achieve an accuracy comparable to other assemblers, this is not a favorable solution.

Fermi is designed to be used on a multi-core Linux machine with large shared memory. The easiest way to run fermi is to use the run-fermi.pl script. It generates a Makefile. The actual assembly is done by invoking make. Premature assembly processes can be resumed. Here is an example:

run-fermi.pl -dAPe ./fermi -p NA12878 -t16 -f18 reads*.fq.gz > NA12878.mak
make -f NA12878.mak -j16

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