BOL: Related items

Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

Jit — Thu, 15 Feb 2018 10:21:02 -0600

Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the k-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10⁹ bp).
Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.

A manual can be found here.

Address of the bookmark: https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

Jit — Wed, 14 Nov 2018 04:50:27 -0600

MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph (SdBG) to achieve low memory assembly. MEGAHIT can optionally utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.

https://academic.oup.com/bioinformatics/article/31/10/1674/177884

Address of the bookmark: https://github.com/voutcn/megahit

PSPairwise Sequentially Markovian Coalescent (PSMC) model

Jit — Thu, 19 Apr 2018 05:29:23 -0500

Implementation of the Pairwise Sequentially Markovian Coalescent (PSMC) model

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

miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set

Abhimanyu Singh — Thu, 16 Aug 2018 04:48:07 -0500

From list of genes... ... to microRNAs

Using established miRNA-interaction databases, miREM combines hypergeometric and expectation-maximization algorithms to sieve through your genetic data for microRNA signatures

https://bioinfo-csi.nus.edu.sg/mirem2/

Address of the bookmark: https://bioinfo-csi.nus.edu.sg/mirem2/

AnchorWave

Neel — Wed, 23 Feb 2022 08:14:35 -0600

AnchorWave (Anchored Wavefront Alignment) identifies collinear regions via conserved anchors (full-length CDS and full-length exon have been implemented currently) and breaks collinear regions into shorter fragments, i.e., anchor and inter-anchor intervals. By performing sensitive sequence alignment for each shorter interval via a 2-piece affine gap cost strategy and merging them together, AnchorWave generates a whole-genome alignment for each collinear block. AnchorWave implements commands to guide collinear block identification with or without chromosomal rearrangements and provides options to use known polyploidy levels or whole-genome duplications to inform alignment.

Address of the bookmark: https://github.com/baoxingsong/AnchorWave

CroCo: A program to detect potential cross contaminations in HTS assembled transcriptomes using expression level quantification

Jit — Mon, 07 Jan 2019 18:17:44 -0600

CroCo is a program to detect cross contamination events in assembled transcriptomes using sequencing reads to determine the true origin of every transcripts.
Such cross contaminations can be expected if several RNA-Seq experiments were prepared during the same period at the same lab, or by the same people, or if they were processed or sequenced by the same sequencing service facility.
Our approach first determines a subset of transcripts that are suspiciously similar across samples using a pairwise BLAST procedure. CroCo then combine all transcriptomes into a metatranscriptome and quantifies the "expression level" of all transcripts successively using every sample read data (e.g. several species sequenced by the same lab for a particular study) while allowing read multi-mappings.
Several mapping tools implemented in CroCo can be used to estimate expression level (default is RapMap).
This information is then used to categorize each transcript in the following 5 categories :

clean: the transcript origin is from the focal sample.

cross contamination: the transcript origin is from an alien sample of the same experiment.

dubious: expression levels are too close between focal and alien samples to determine the true origin of the transcript.

low coverage: expression levels are too low in all samples, thus hampering our procedure (which relies on differential expression) to confidently assign it to any category.

over expressed: expression levels are very high in at least 3 samples and CroCo will not try to categorize it. Indeed, such a pattern does not correspond to expectations for cross contaminations, but often reflect highly conserved genes such as ribosomal gene, or external contamination shared by several samples (e.g. Escherichia coli contaminations).

Address of the bookmark: https://gitlab.mbb.univ-montp2.fr/mbb/CroCo

FSA: Fast Statistical Alignment

Jit — Mon, 06 Feb 2017 04:26:01 -0600

FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in "Posterior Decoding Methods for Optimization and Control of Multiple Alignments."

FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:

FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.
FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.
FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.
The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and movie at the top of the page).

You can see more information on the FAQ.

Address of the bookmark: http://fsa.sourceforge.net/

DADA2: Fast and accurate sample inference from amplicon data with single-nucleotide resolution

Jit — Tue, 10 Nov 2020 20:26:00 -0600

The DADA2 tutorial goes through a typical workflow for paired end Illumina Miseq data: raw amplicon sequencing data is processed into the table of exact amplicon sequence variants (ASVs) present in each sample.

The DADA2 Workflow on Big Data goes through workflow optimized to run on large datasets (10s of millions to billions of reads).

An ITS-specific version of the DADA2 workflow identifies and verifiably removes primers on both ends of each ITS read, a key step due to the variable length of the ITS region.

Short demonstrations of assigning taxonomy and assigning species to sequences.

Address of the bookmark: https://benjjneb.github.io/dada2/index.html

LAMSA: fast split read alignment with long approximate matches

Jit — Tue, 15 May 2018 04:44:42 -0500

LAMSA (Long Approximate Matches-based Split Aligner) is a novel split alignment approach with faster speed and good ability of handling SV events. It is well-suited to align long reads (over thousands of base-pairs). LAMSA takes takes the advantage of the rareness of SVs to implement a specifically designed two-step strategy. That is, LAMSA initially splits the read into relatively long fragments and co-linearly align them to solve the small variations or sequencing errors, and mitigate the effect of repeats. The alignments of the fragments are then used for implementing a sparse dynamic programming (SDP)-based split alignment approach to handle the large or non-co-linear variants. We benchmarked LAMSA with simulated and real datasets having various read lengths and sequencing error rates, the results demonstrate that it is substantially faster than the state-of-the-art long read aligners; mean-while, it also has good ability to handle various categories of SVs. LAMSA is open source and free for non-commercial use. LAMSA is mainly designed by Bo Liu & Yan Gao and developed by Yan Gao in Center for Bioinformatics, Harbin Institute of Technology, China.

Address of the bookmark: https://github.com/hitbc/LAMSA

LSC :a long read error correction tool

Jit — Thu, 02 Aug 2018 07:39:46 -0500

Getting Started

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

Read the LSC_requirements for running LSC.
Download and set-up the LSC package.
Follow the tutorial to see how LSC works on some example data.
Read the manual if anything is unclear.
You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript]
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/