BOL: Related items

RefKA: A fast and efficient long-read genome assembly approach for large and complex genomes

Rahul Nayak — Fri, 01 May 2020 03:00:40 -0500

RefKA, a reference-based approach for long read genome assembly. This approach relies on breaking up a closely related reference genome into bins, aligning k-mers unique to each bin with PacBio reads, and then assembling each bin in parallel followed by a final bin-stitching step.

Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

merqury: Evaluate genome assemblies with k-mers

Jit — Fri, 03 Jul 2020 19:29:34 -0500

Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.

More at https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full

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

HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding.

Jit — Mon, 26 Oct 2020 21:23:36 -0500

Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.

More at https://github.com/esolares/HapSolo

Address of the bookmark: https://github.com/esolares/HapSolo

InfoGenomeR: Integrative reconstruction of cancer genome karyotypes

Jit — Wed, 05 May 2021 01:02:18 -0500

InfoGenomeR is the Integrative Framework for Genome Reconstruction that uses a breakpoint graph to model the connectivity among genomic segments at the genome-wide scale. InfoGenomeR integrates cancer purity and ploidy, total CNAs, allele-specific CNAs, and haplotype information to identify the optimal breakpoint graph representing cancer genomes.

More at https://www.nature.com/articles/s41467-021-22671-6

Address of the bookmark: https://github.com/dmcblab/InfoGenomeR

Understanding kmer !

BioStar — Wed, 18 Aug 2021 04:27:51 -0500

What is a k-mer anyway? A k-mer is just a sequence of k characters in a string (or nucleotides in a DNA sequence). Now, it is important to remember that to get all k-mers from a sequence you need to get the first k characters, then move just a single character for the start of the next k-mer and so on. Effectively, this will create sequences that overlap in k-1 positions.

Address of the bookmark: https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/

maftools

Surabhi Chaudhary — Fri, 17 Dec 2021 03:18:28 -0600

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widely accepted and used to store somatic variants detected. The Cancer Genome Atlas Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. Resulting data consisting of somatic variants are stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.

https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html

Address of the bookmark: https://github.com/PoisonAlien/maftools

Short-read assembly using Spades !

Abhimanyu Singh — Mon, 31 Jan 2022 07:18:16 -0600

If we only had Illumina reads, we could also assemble these using the tool Spades.

You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

illumina_R1.fastq.gz: the Illumina forward reads
illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

-1 is input file of forward reads
-2 is input file of reverse reads
--careful minimizes mismatches and short indels
--cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
-o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

Bioinfo Lab

Fri, 04 Mar 2022 00:17:00 -0600

The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.

Contact:
Computer Science Building (Science Park 3)
Altenberger Str. 69, A-4040 Linz, Austria
Tel. +43 732 2468 4520 / Fax +43 732 2468 4539
E-mail secretary@bioinf.jku.at

http://www.bioinf.jku.at/

genomenotebook

Abhi — Thu, 20 Apr 2023 13:19:01 -0500

https://dbikard.github.io/genomenotebook/

Install

pip install genomenotebook

How to use

Create a simple genome browser with a search bar. The sequence appears when zooming in.

import genomenotebook as gn

g=gn.GenomeBrowser(genome_path, gff_path, init_pos=10000)
g.show()

Tracks can be added to visualize your favorite genomics data. See Examples for more !!!!

Address of the bookmark: https://dbikard.github.io/genomenotebook/

Mitochondrial genome assembly tools !

Abhi — Wed, 06 Sep 2023 00:37:18 -0500

Mitochondrial genome assembly tools are specialized software and algorithms designed to accurately reconstruct the mitochondrial genome (mitogenome) from sequencing data, typically obtained through techniques like next-generation sequencing (NGS). The mitochondrial genome is relatively small compared to the nuclear genome, making it an ideal target for assembly. Here are some commonly used mitochondrial genome assembly tools:

MitoFinder: Mitofinder is a pipeline to assemble mitochondrial genomes and annotate mitochondrial genes from trimmed read sequencing data.

MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads

MITObim: MITObim is a tool specifically developed for the iterative assembly of mitochondrial genomes. It starts with a reference mitogenome and iteratively refines the assembly using the read data.

MITOS: MITOS is a web-based platform that provides a pipeline for annotating mitochondrial genomes. It integrates multiple software tools for assembly, annotation, and visualization of mitogenomes.

MIRA: MIRA (Mimicking Intelligent Read Assembly) is a versatile genome assembly tool that can be used for mitochondrial genome assembly. It supports various sequencing technologies and allows for reference-based or de novo assembly.

NOVOPlasty: NOVOPlasty is a user-friendly tool designed for de novo assembly of organelle genomes, including mitochondria. It utilizes a seed-and-extend algorithm and is suitable for both short-read and long-read data.

MITOS2: MITOS2 is an updated version of the MITOS pipeline, which automates the annotation of mitochondrial genomes. It provides improved accuracy and additional features for mitochondrial genome analysis.

GetOrganelle: While primarily designed for chloroplast genome assembly, GetOrganelle can also be used for mitochondrial genome assembly. It is particularly useful for dealing with high-throughput sequencing data.

SPAdes: SPAdes (St. Petersburg genome assembler) is a versatile genome assembly tool that can be employed for mitochondrial genome assembly, especially when dealing with complex datasets that may contain nuclear mitochondrial DNA sequences (numts).

IDBA-UD: IDBA-UD (Iterative De Bruijn Graph De Novo Assembler) is another de novo assembly tool that can be used for mitochondrial genome assembly, especially in cases with relatively low coverage.

Velvet: Velvet is a de novo assembly tool that can be applied to mitochondrial genome assembly, especially when working with short-read data.

When selecting a mitochondrial genome assembly tool, it's important to consider the specific characteristics of your sequencing data, such as read length and coverage, as well as the complexity of the mitochondrial genome. Additionally, some tools are better suited for specific organisms or research objectives, so choosing the right tool will depend on your particular project requirements.