Herald:The Biostar Herald for Monday, February 06, 2023
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The Biostar Herald publishes user submitted links of bioinformatics relevance. It aims to provide a summary of interesting and relevant information you may have missed. You too can submit links here.

This edition of the Herald was brought to you by contribution from Istvan Albert, and was edited by Istvan Albert,


DandD: efficient measurement of sequence growth and similarity | bioRxiv (www.biorxiv.org)

Genome assembly databases are growing rapidly. The sequence content in each new assembly can be largely redundant with previous ones, but this is neither conceptually nor algorithmically easy to measure. We propose new methods and a new tool called DandD that addresses the question of how much new sequence is gained when a sequence collection grows. DandD can describe how much human structural variation is being discovered in each new human genome assembly and when discoveries will level off in the future. DandD uses a measure called delta, developed initially for data compression.

submitted by: Istvan Albert


When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes | bioRxiv (www.biorxiv.org)

We benchmarked state-of-the-art long-read de novo assemblers, to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 13 real and 72 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio CLR, PacBio HiFi, and ONT sequencing to evaluate the assemblers. We include five commonly used long read assemblers in our benchmark: Canu, Flye, Miniasm, Raven and Redbean.

submitted by: Istvan Albert


Just a moment... (royalsocietypublishing.org)

The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing—no deliberate cheating nor loafing—by scientists, only that publication is a principal factor for career advancement.

submitted by: Istvan Albert


Structural variation across 138,134 samples in the TOPMed consortium | bioRxiv (www.biorxiv.org)

Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium.

submitted by: Istvan Albert


[2210.10341] BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (arxiv.org)

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e., BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our larger model BioGPT-Large achieves 81.0% on PubMedQA. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

submitted by: Istvan Albert


GitHub - microsoft/BioGPT (github.com)

This repository contains the implementation of BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining, by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.

submitted by: Istvan Albert


GitHub - WGLab/doc-ANNOVAR: Documentation for the ANNOVAR software (github.com)

ANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, hg38, as well as mouse, worm, fly, yeast and many others).

This is the GitHub repository for the documentation of the ANNOVAR software, described in the paper listed below. Any edit to this repository will be reflected at ANNOVAR home page at http://annovar.openbioinformatics.org instantly.

submitted by: Istvan Albert


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