BOL: Related items

EAGLER: a scaffolding tool for long reads.

Jit — Mon, 04 Jun 2018 05:26:03 -0500

EAGLER is a scaffolding tool for long reads. The scaffolder takes as input a draft genome created by any NGS assembler and a set of long reads. The long reads are used to extend the contigs present in the NGS draft and possibly join overlapping contigs. EAGLER supports both PacBio and Oxford Nanopore reads.

The tool should be compatible with most UNIX flavors and has been successfully tested on the following operating systems:

Mac OS X 10.11.1
Mac OS X 10.10.3
Ubuntu 14.04 LTS

https://bib.irb.hr/datoteka/844447.Diplomski_2015_Luka_terbi.pdf

Address of the bookmark: https://github.com/mculinovic/EAGLER

ReMILO: reference assisted misassembly detection algorithm using short and long reads.

Jit — Fri, 06 Jul 2018 04:27:49 -0500

ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.

Address of the bookmark: https://github.com/songc001/remilo

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

Rebaler: program for conducting reference-based assemblies using long reads.

Jit — Tue, 18 Sep 2018 07:52:41 -0500

Rebaler is a program for conducting reference-based assemblies using long reads. It relies mainly on minimap2 for alignment and Racon for making consensus sequences.

I made Rebaler for bacterial genomes (specifically for the task of testing basecallers). It should in principle work for non-bacterial genomes as well, but I haven't tested it.

Address of the bookmark: https://github.com/rrwick/Rebaler

wtdbg2: A fuzzy Bruijn graph approach to long noisy reads assembly

BioStar — Mon, 04 Feb 2019 04:53:47 -0600

Wtdbg2 is a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output.

./wtdbg2 -x rs -g 4.6m -t 16 -i reads.fa.gz -fo prefix
./wtpoa-cns -t 16 -i prefix.ctg.lay.gz -fo prefix.ctg.fa

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

Filtlong: quality filtering tool for long reads

Radha Agarkar — Wed, 13 May 2020 10:23:55 -0500

Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Filtlong builds into a stand-alone executable:

git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h

Address of the bookmark: https://github.com/rrwick/Filtlong

MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)

Jit — Tue, 12 Dec 2017 17:23:31 -0600

MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a k-mer based Jaccard similarity using a combination of Winnowing and MinHash. This is then converted to an estimate of sequence identity using the Mash distance. An appropriate k-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.

Address of the bookmark: https://github.com/marbl/MashMap

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/

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules

Pacasus: Correction of palindromes in long reads from PacBio and Nanopore

BioStar — Mon, 12 Nov 2018 05:26:48 -0600

Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus