BOL: Related items

FLAS: fast and high throughput algorithm for PacBio long read self-correction.

Jit — Sat, 22 Jun 2019 12:16:39 -0500

FLAS, a wrapper algorithm of MECAT, to achieve high throughput long read self-correction while keeping MECAT's fast speed. FLAS finds additional alignments from MECAT prealigned long reads to improve the correction throughput, and removes misalignments for accuracy.

Address of the bookmark: https://github.com/baoe/flas

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

Jit — Sun, 15 Dec 2019 10:19:35 -0600

The Docker image is available at https://hub.docker.com/r/biodatageeks/. Supplementary information on benchmarking procedure as well as test data are publicly accessible at the project documentation site http://biodatageeks.org/sequila/benchmarking/benchmarking.html#depth-of-coverage. An archival copy of the code and supporting data is also available via the GigaScience database GigaDB

• Project name: SeQuiLa-cov

• Project home page: http://biodatageeks.org/sequila/

• Source code repository: https://github.com/ZSI-Bio/bdg-sequila

• Operating system: Platform independent

• Programming language: Scala

• Other requirements: Docker

• License: Apache License 2.0

Address of the bookmark: https://academic.oup.com/gigascience/article/8/8/giz094/5543653

fastv - detect virus

Jit — Sat, 11 Dec 2021 08:04:10 -0600

fastv is an ultra-fast tool for identification of SARS-CoV-2 and other microbes from sequencing data. It detects microbial sequences from FASTQ data, generates JSON reports and visualizes the result in HTML reports. This tool can be used to detect viral infectious diseases, like COVID-19. This tool supports both short reads (Illumina, BGI, etc.) and long reads (ONT, PacBio, etc.)

Address of the bookmark: https://github.com/OpenGene/fastv

HELIANO: A fast and accurate tool for detection of Helitron-like elements

LEGE — Tue, 13 Aug 2024 07:16:34 -0500

Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.

https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true

Address of the bookmark: https://github.com/Zhenlisme/heliano/

Efficient genome searching with Biostrings and the BSgenome data package

Aasha — Mon, 07 Mar 2016 05:18:06 -0600

Address of the bookmark: https://www.bioconductor.org/packages/3.3/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf

Common Bioinformatics Interview Questions !

Jit — Sat, 23 Jan 2021 06:07:50 -0600

The possibility of an interview for a bioinformatics position in the life sciences may be very disquieting, but the same concerns emerge time and again in my experience. So, it is exceedingly worthwhile to plan for future bioinformatics interview questions. Doing this will really give you the advantage in obtaining the position.

The following 5 questions are those that I have heard many times during the job-search process. There is no reason for not planning responses in such situations.

1. Tell Us About Yourself
This is a very typical opener in interviews. It's a perfect question to ask, and getting something planned will really help you concentrate and ease in the conversation. However, you need to make sure that your response is applicable to the job you're interviewing.
It's probably better to keep your answer professional. Try to include these in the answer as well: where did your love of science and bioinformatics come from? How the heck did you end up in this field? Why programming and scripting ?

2. What is your plan for your bioinformatics career? / How do you look at yourself in five years? / How are your personal objectives to accomplish these goals / What are the plan for your research fundings ?

Your CV/resume has already impressed the selection panel if you have been invited for an interview. The questions from the bioinformatics interview team provide an incentive for you to market yourself and illustrate the work in question with the most appropriate knowledge.

3. What do you understand about the job description/What would your suggested research path be if you were a successful candidate?
Summarize the specifics of the advertised bioinformatics position in your own words. Follow on with some suggestions of how you want to extend your research and create your own projects within the community.

4. Will you work as a group or do you want to work on your own?
This requirement can vary from jobs to job, so when addressing, be alert. A company/research PI may need a bioinformatician that is able to work on a single project autonomously, or they may need a person who can help direct and organize a team. In your response, refer to the job description.

5. What particular methods have you used to date with your experiments?
You might have experience with all the laboratory techniques described in the job description, but stress the ones you highly experienced with. Highlight your professional abilities and stress that you are extremely capable of mastering new techniques with others ...

At the end of the day, remember that you're questioning the jury as well as they're interviewing you. You will ought to think of any questions you would like the interview panel to pose. This indicates that you have done your homework and serious about the position.

All the best for your future job interview.

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads

Jit — Wed, 07 Jun 2017 04:18:16 -0500

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads http://www.nature.com/ncomms/2016/160415/ncomms11307/full/ncomms11307.html

Features

    Mapping position agnostic to alignment parameters.
    Consistently very high sensitivity and precision across different error profiles, rates and sequencing technologies even with default parameters.
    Circular genome handling to resolve coverage drops near ends of the genome.
    E-value.
    Meaningful mapping quality.
    Various alignment strategies (semiglobal bit-vector and Gotoh, anchored).
    Overlapping of reads for de novo assembly.
    Transcriptome mapping through internal construction of a transcriptome from a given genomic reference and a GTF file.
    ...and much more.

GraphMap is also used as an overlapper in a new de novo genome assembly project called Ra (https://github.com/mariokostelac/ra-integrate).
Ra attempts to create de novo assemblies from raw nanopore and PacBio reads without requiring error correction, for which a highly sensitive overlapper is required.

Currently, development of a new spliced-alignment mode for mapping RNA-seq reads is under way.
Description of the current effort as well as how to reach the experimental implementation can be found here: doc/rnaseq.md.

Address of the bookmark: https://github.com/isovic/graphmap

Sequencing By Xpansion

Jitendra Narayan — Wed, 17 Jun 2015 20:58:11 -0500

Sequencing By Xpansion (SBX) is a DNA sequencing method that uses a simple biochemical reaction to encode the sequence of a DNA molecule into a highly measurable surrogate called an Xpandomer. This single molecule approach produces enough Xpandomer in a single drop reaction to sequence an entire human genome 1000X over. To achieve this, an Xpandomer replaces each DNA sequence with a sequence of large, high signal reporter molecules using the SBX molecular expansion technology. The DNA sequence is then read out as the Xpandomer reporters pass sequentially through a nanopore detector. SBX is a molecular engineering platform that benefits from core design principles that separate the multiple molecular functions. This systems approach enables efficient development and incorporation of improvements to SBX and is key to reconfiguring and optimizing Xpandomer measurement for different detection platforms.

http://www.stratosgenomics.com/stratos-genomics-technology

methylKit

Jit — Fri, 03 Jun 2016 10:09:29 -0500

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.

Address of the bookmark: https://github.com/al2na/methylKit

Kaiju

Jit — Mon, 27 Jun 2016 11:23:04 -0500

Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/