BOL: Related items

HairSplitter: assembling long reads in an unknown number of haplotypes

BioStar — Wed, 07 Dec 2022 00:13:40 -0600

Pros and cons of HairSplitter Limitations of HairSplitter:

Not very fast: it re-polishes the whole assembly

Limited in the number of haplotypes

Strengths of HairSplitter:

Very modular, can be used with any assembler

Naive: makes no assumption on ploidy, parameter-free

Safe: won’t artificially duplicate contigs

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

Not really available yet (github.com/RolandFaure/HairSplitter)

https://hal.archives-ouvertes.fr/hal-03864075/file/RolandFaure_presentation_SeqBIM_2022.pdf

Address of the bookmark: https://hal.archives-ouvertes.fr/hal-03817928/document

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

Jabba: Hybrid Error Correction for Long Sequencing Reads

Jit — Fri, 05 Jan 2018 03:58:14 -0600

Jabba is a hybrid error correction tool to correct third generation (PacBio / ONT) sequencing data, using second generation (Illumina) data.

Input

Jabba takes as input a concatenated de Bruijn graph and a set of sequences:

the de Bruijn graph should appear in fasta format with 1 entry per node, the meta information should be in the format:
>NODE
the set of sequences should be in fasta or fastq format. These sequences will be corrected (e.g. PacBio reads). The corrections will be written to a file Jabba fasta.
The output is a file in fasta format with corrections of the long reads, and additionally a file in the input format containing uncorrected reads.

https://github.com/biointec/jabba/wiki

https://almob.biomedcentral.com/articles/10.1186/s13015-016-0075-7

Address of the bookmark: https://github.com/biointec/jabba

Porechop: tool for finding and removing adapters from Oxford Nanopore reads

Rahul Nayak — Tue, 29 May 2018 07:33:44 -0500

Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

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

lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data

BioJoker — Tue, 27 Nov 2018 04:43:57 -0600

lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

Address of the bookmark: https://github.com/vpc-ccg/lordfast

Chekulaevalab

Tue, 12 Jan 2016 02:32:03 -0600

Focusing on understanding the molecular mechanisms that regulate mRNA translation, localization and stability and role of non-coding RNAs in this process. Up to 90% of human DNA is estimated to be transcribed into so called non-coding RNAs that are not translated into proteins. Many of them act as potent modifiers of gene expression. miRNAs are a class of such short non-coding RNAs. They regulate expression of more than a half of eukaryotic genes, thus, affecting multiple biological processes, including cell proliferation, differentiation, apoptosis and senescence. Not surprisingly, miRNAs are involved in many human pathologies, including cancer and neurological disorders and hold great potential as drug targets, disease markers, as well as therapeutic agents.
Our lab is located at the Berlin Institute for Medical Systems Biology (BIMSB), a part of the Max Delbrück Center for Molecular Medicine (MDC).

http://www.chekulaevalab.org/

Next Generation Sequencing (NGS) Tutorials

Jitendra Narayan — Sat, 24 Aug 2013 06:01:37 -0500

Institute of computational biomedicine, Cornell University provide an NGS workshop tutorial at http://chagall.med.cornell.edu/NGScourse/

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit.

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

Detecting and annotating genetic variations using the HugeSeq pipeline DOI: 10.1038/nbt.2134
NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
RseqFlow: Workflows for RNA-Seq data analysis DOI: 10.1093/bioinformatics/btr441
ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence 10.1186/1471-2164-12-285
A framework for variation discovery and genotyping using next-generation DNA sequencing data PubMed: 21478889
SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ".

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

BUSCO

Jitendra Narayan — Sun, 07 Feb 2016 16:02:39 -0600

Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

More at http://busco.ezlab.org/

Address of the bookmark: http://busco.ezlab.org/

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

dnaPipeTE: de-novo assembly & annotation Pipeline for Transposable Elements

Rahul Nayak — Sat, 02 Dec 2017 18:25:44 -0600

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en
You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE