BOL: Related items

Hifiasm: a haplotype-resolved assembler for accurate Hifi reads

Jit — Thu, 24 Dec 2020 10:03:36 -0600

Hifiasm is a fast haplotype-resolved de novo assembler for PacBio Hifi reads. It can assemble a human genome in several hours and works with the California redwood genome, one of the most complex genomes sequenced so far. Hifiasm can produce primary/alternate assemblies of quality competitive with the best assemblers. It also introduces a new graph binning algorithm and achieves the best haplotype-resolved assembly given trio data.

Address of the bookmark: https://github.com/chhylp123/hifiasm

Which of the followings are the best place to study Bioinformatics ?

Reshma Khatun — Sun, 28 Dec 2014 00:20:30 -0600

Bioinformatics is a major growth area and qualified Bioinformaticians are in high demand. An explosion in biological data has resulted from genome projects, next generation sequencing and other 'omics' techniques. Bioinformatics provides the tools to analyse and exploit such data sets.

Can you please suggest me the best place to study bioinformatics ( Grad/PostGrad).

Bioinformatics tools for telomere to telomere assembly !

BioStar — Tue, 17 Aug 2021 13:17:09 -0500

● Merfin – k-mer-based assembly and variant calling evaluation for improved consensus accuracy (Arang Rhie)
● PanGenie – algorithm that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation (Tobias Marschall)
● SQANTI3 – an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline (Rocío Amorín de Hegedüs @rocioadh)
● tama (Transcriptome Annotation by Modular Algorithms) – software designed for processing Iso-Seq data and other long-read transcriptome data (Richard Kuo @GenomeRIK)
● pbaa (PacBio Amplicon Analysis) – separates complex mixtures of amplicon targets from genomic samples to cluster and generate high-quality consensus sequences from HiFi reads (Zev Kronenberg @zevkronenberg)
● bellerophon – analyzes MHC typing and other low-complexity gene amplicon data; performs allele calling while detecting polymorphic sites within the sequences and removing potential chimeric sequence variants (Yuanyuan Cheng @Yuanyuan929)
● svpack – tools for filtering, comparing, and annotating structural variant (SV) calls in VCF format (Aaron Wenger)
● JumboDB – tool for de Bruijn graph construction (Anton Bankevich @AntonBankevich)
● uLTRA – tool for splice alignment of long transcriptomic reads to a genome, guided by a database of exon annotations. (Kristoffer Sahlin @krsahlin)
● LeafGo – workflow to rapidly produce high-quality de novo plant genomes (Luca Ermini @ermini_luca)

Reference:

https://www.pacb.com/blog/young-investigators-share-stellar-science-career-advice-and-bioinformatics-tools-at-smrt-leiden-2021/

Roche has acquired Bina Technologies !!!

Jit — Tue, 30 Dec 2014 09:42:16 -0600

Bina Technologies is a privately held company that provides a big data platform for centralized management and processing of next generation sequencing (NGS) data for the academic and translational research markets. Bina will be integrated into the Roche Sequencing Unit, and will continue to focus on development of their innovative genomic analysis solution.

Roche has acquired Bina Technologies, a privately-owned biotech company based in California. The biotech’s first product was the Bina Box, a platform for secondary genomic analysis, sequence alignment, and variant calling, but since 2012, it has developed other products and platforms.

It is our shared vision with Roche that informatics and data sciences are critical elements of an end-to-end genomics solution. Fast, easy-to-use, scalable, and robust informatics solutions make a big difference in the quality and impact of the work of scientists and researchers. We believe in the future of data-driven, personalized medicine. We are passionate about accelerating that future together with Roche.

Financial details of the deal were not disclosed. For Roche, the move is yet another in a string of acquisitions. Last week (December 18), Roche paid $489 million for antibody maker Dutalys. And earlier this month, Roche bought prenatal testing company Ariosa Diagnostics.

Reference

http://blog.bina.com/news/bina-technologies-acquired-by-roche?&__hssc=109677338.1.1419953400266&__hstc=109677338.b8350f2729889b08f1325906d5236cd3.1419953400266.1419953400266.1419953400266.1&hsCtaTracking=96cac941-9372-4bbf-bacb-3ca6f1ff8cfd|3fce0f18-835b-4086-9345-388880861732

http://www.the-scientist.com/?articles.view/articleNo/41750/title/Roche-Buys-Bioinformatics-Firm/

Steps to find palindrome in genomes !

BioStar — Thu, 09 Mar 2023 02:56:54 -0600

Palindromes are sequences of nucleotides that read the same backward as forward. They can be present in genomes and have various biological functions. Here are some methods for discovering palindromes in genomes:

Direct sequence search: One of the simplest ways to discover palindromes is to search the genome sequence directly for palindromic sequences using pattern matching tools, such as regular expressions or string algorithms. This approach can be useful for discovering simple palindromes, but may miss more complex palindromic structures.
Dot plot analysis: Dot plot analysis is a graphical method that can be used to identify palindromic regions in a genome. It involves plotting the genome sequence against itself and examining the diagonal patterns that emerge. Palindromic regions will appear as symmetrical patterns along the diagonal.
Restriction enzyme analysis: Some restriction enzymes, such as EcoRI and HindIII, recognize palindromic sequences and cleave DNA at these sites. By digesting the genome with these enzymes and examining the resulting fragments, palindromic regions can be identified.
Next-generation sequencing: High-throughput sequencing technologies, such as PacBio and Oxford Nanopore, can generate long reads that can span entire palindromic regions. By mapping these reads to the genome, palindromic regions can be identified and characterized.
Comparative genomics: Comparing the genomes of related species can also reveal palindromic regions that are conserved across evolutionarily divergent lineages. This approach can help identify functional palindromes that are under selective pressure.

Overall, the discovery of palindromic sequences in genomes can be accomplished using a variety of methods, each with their own advantages and limitations. A combination of these methods can provide a comprehensive understanding of the palindromic landscape of a genome.

Type Hinting

Pranjali Yadav — Fri, 09 Jan 2015 22:26:13 -0600

Python creator Guido van Rossum’s proposal for static type-checking annotations is inching closer to reality, and the feature has taken on a new name: type hinting.

Back in August, van Rossum published a proposal on the Python mailing list recommending type-checking annotations as a valuable feature for the next version of Python to improve the performance of editors and IDEs, linter capabilities, standard notation, and refactoring. Van Rossum’s latest proposal, posted late last month, outlined plans to publish a Python Enhancement Proposal (PEP) in early January to put the feature now known as type hinting on track for inclusion in Python 3.5, slated for release this September.

Reference

https://quip.com/r69HA9GhGa7J

QuorUM: An Error Corrector for Illumina Reads

Jit — Wed, 08 Nov 2017 11:40:41 -0600

Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term “error correction” to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.

QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821

Postdoctoral Researcher in Cancer Systems Biology

Mon, 12 Jan 2015 01:44:11 -0600

Postdoctoral Researcher in Cancer Systems Biology
Department of Oncology, Old Road Campus Research Building, Roosevelt Drive, Oxford
Grade 7: £30,434 - £37,394 with a discretionary range to £40,847 p.a.
Applications are invited for a Postdoctoral Researcher in Cancer Systems Biology to join a rapidly developing Bioinformatics Research Core group headed by Dr Anastasia Samsonova. The purpose of the role is to develop and deliver integrative approaches to dissect the complexity of cancer as a genomic disease. The research will focus on development and application of effective strategies for mining and integration of complex human *omics datasets and clinical/phenotypic data in cancer studies.

The role sits at the critical interface between genetics and cancer systems biology, and would suit a candidate who is interested in developing a career at the confluence of Statistics/Data Mining/Machine Learning and Biology. Ideally, you will have experience in development of analytical approaches to high-throughput and multivariate data mining and integration gained through a PhD (or equivalent) in a quantitative subject (eg mathematics, statistics, physics, engineering or computer science).

Experience of statistics and/or machine learning techniques is highly desirable as is evidence of prior experience of developing bioinformatics software and/or analysing complex *omics data sets. You will be able to work as part of a team and independently and deliver results to the required standard and schedule. You should be able to organise and prioritise your own work, as well as have excellent communication skills, both written and oral. The post will involve interactions with collaborators from such diverse fields as applied mathematics, statistics, computer science and medicine.

This is a full-time post, fixed-term until 31 March 2017. For informal enquiries, contact Dr Anastasia Samsonova (bioinformatics@oncology.ox.ac.uk).

All applicants must complete a short application form and upload a CV and supporting statement.

The closing date for applications is 12.00 noon on 26 January 2015.

More at https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.display_form

NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads

BioStar — Thu, 24 Jan 2019 10:35:12 -0600

NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.

https://nxrepair.readthedocs.io/en/latest/tutorial.html

nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta

Address of the bookmark: https://github.com/rebeccaroisin/nxrepair

Interactive Market Intelligence

Pranjali Yadav — Mon, 19 Jan 2015 08:20:38 -0600

BioInformatics LLC, a premier research and advisory firm serving the life science industry, has launched groundbreaking, dynamic-data presentation platform, Interactive Market Intelligence— the only cloud-based market research analytics tool for the life science tools industry.

Superior to traditional PDF and PowerPoint reports, Interactive Market Intelligence allows end-users to filter, create and export literally thousands of views of data — all easily obtainable from a set of core metrics that include market, brand, customer and workflow analytics in well-defined segments of the life science market.

The Market for Real-Time PCR is the first in a series of topics to be explored using the Interactive Market Intelligence platform. The primary research analysis is based on a survey of 900+ international scientists performing qPCR in their laboratories.

Key data findings from "The Market for Real-Time PCR": Global market for qPCR in 2015 is estimated to be $3.6B; The average growth in qPCR throughput is expected to be at 9.8% in 2015; 22% of respondents are highly likely to switch primary suppliers of qPCR products; 50% of respondents use pre-designed primer/probe sets.