Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

/pdf/highly-accurate-protein-structure-prediction-with-alphafold-2xjbzfwu3n.pdf

Highly accurate protein structure prediction with AlphaFold

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.

Pfam: The protein families database in 2021.

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (
 https://github.com/marbl/mash
 ).

/pdf/mash-fast-genome-and-metagenome-distance-estimation-using-3b84vfnedh.pdf

Mash: fast genome and metagenome distance estimation using MinHash.

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

/pdf/entity-linking-with-a-knowledge-base-issues-techniques-and-2ds9mn4o8g.pdf

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

Counting all k -mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k -mer databases. Usefulness of the tools is shown on a few real problems.Program is freely available at http://sun.aei.polsl.pl/REFRESH/kmc .sebastian.deorowicz@polsl.pl.Supplementary data are available at Bioinformatics online.

KMC 3: counting and manipulating k-mer statistics.

Motivation Building the histogram of occurrences of every k-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of k-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for k-mer counting, preferably using moderate amounts of memory. Results We present a novel method for k-mer counting, on large datasets about twice faster than the strongest competitors (Jellyfish 2, KMC 1), using about 12 GB (or less) of RAM. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using (k, x)-mers allows to significantly reduce the I/O and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC 2 counts the 28-mers of a human reads collection with 44-fold coverage (106 GB of compressed size) in about 20 min, on a 6-core Intel i7 PC with an solid-state disk.

KMC 2: fast and resource-frugal k-mer counting

Motivation: Modern sequencing instruments are able to generate at least hundreds of millions short reads of genomic data. Those huge volumes of data require effective means to store them, provide quick access to any record and enable fast decompression.

Results: We present a specialized compression algorithm for genomic data in FASTQ format which dominates its competitor, G-SQZ, as is shown on a number of datasets from the 1000 Genomes Project ( www.1000genomes.org).

Availability: DSRC is freely available at http:/sun.aei.polsl.pl/dsrc.

Contact: sebastian.deorowicz@polsl.pl

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/compression-of-dna-sequence-reads-in-fastq-format-5exqx71bki.pdf

Compression of DNA sequence reads in FASTQ format

Summary: Counting all k-mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k-mer databases. Usefulness of the tools is shown on a few real problems. Availability: Program is freely available at this http URL. Contact: sebastian.deorowicz@polsl.pl

KMC 3: counting and manipulating k-mer statistics

Motivation: Storing, transferring and maintaining genomic databases becomes a major challenge because of the rapid technology progress in DNA sequencing and correspondingly growing pace at which the sequencing data are being produced. Efficient compression, with support for extraction of arbitrary snippets of any sequence, is the key to maintaining those huge amounts of data.

Results: We present an LZ77-style compression scheme for relative compression of multiple genomes of the same species. While the solution bears similarity to known algorithms, it offers significantly higher compression ratios at compression speed over an order of magnitude greater. In particular, 69 differentially encoded human genomes are compressed over 400 times at fast compression, or even 1000 times at slower compression (the reference genome itself needs much more space). Adding fast random access to text snippets decreases the ratio to ~300.

Availability: GDC is available at http://sun.aei.polsl.pl/gdc.

Contact: sebastian.deorowicz@polsl.pl

Supplementary Information:Supplementary data are available at Bioinformatics online.

/pdf/robust-relative-compression-of-genomes-with-random-access-3j9q0pngwa.pdf

Sebastian Deorowicz

Papers

KMC 3: counting and manipulating k-mer statistics.

KMC 2: fast and resource-frugal k-mer counting

Compression of DNA sequence reads in FASTQ format

KMC 3: counting and manipulating k-mer statistics

Robust relative compression of genomes with random access