Biomedical subjects

Sebastian Will

Publications and source records attributed to Sebastian Will.

5 recordsLinked to original sources

PubMed2025

CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

MOTIVATION: Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. RESULTS: In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. AVAILABILITY AND IMPLEMENTATION: CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

SARS-CoV-2↗

PubMed2007

RNAs everywhere: genome-wide annotation of structured RNAs.

Starting with the discovery of microRNAs and the advent of genome-wide transcriptomics, non-protein-coding transcripts have moved from a fringe topic to a central field research in molecular biology. In this contribution we review the state of the art of "computational RNomics", i.e., the bioinformatics approaches to genome-wide RNA annotation. Instead of rehashing results from recently published surveys in detail, we focus here on the open problem in the field, namely (functional) annotation of the plethora of putative RNAs. A series of exploratory studies are used to provide non-trivial examples for the discussion of some of the difficulties.

Biological Evolution↗

PubMed2005

SECISDesign: a server to design SECIS-elements within the coding sequence.

SUMMARY: SECISDesign is a server for the design of SECIS-elements and arbitrary RNA-elements within the coding sequence of an mRNA. The element has to satisfy both structure and sequence constraints. At the same time, a certain amino acid similarity to the original protein has to be kept. The designed sequence can be used for recombinant expression of selenoproteins in Escherichia coli. AVAILABILITY: The server is available at http://www.bio.inf.uni-jena.de/Software/SECISDesign/index.html.

Computer-Aided Design↗

PubMed2004

Local sequence-structure motifs in RNA.

Ribonuclic acid (RNA) enjoys increasing interest in molecular biology; despite this interest fundamental algorithms are lacking, e.g. for identifying local motifs. As proteins, RNA molecules have a distinctive structure. Therefore, in addition to sequence information, structure plays an important part in assessing the similarity of RNAs. Furthermore, common sequence-structure features in two or several RNA molecules are often only spatially local, where possibly large parts of the molecules are dissimilar. Consequently, we address the problem of comparing RNA molecules by computing an optimal local alignment with respect to sequence and structure information. While local alignment is superior to global alignment for identifying local similarities, no general local sequence-structure alignment algorithms are currently known. We suggest a new general definition of locality for sequence-structure alignments that is biologically motivated and efficiently tractable. To show the former, we discuss locality of RNA and prove that the defined locality means connectivity by atomic and non-atomic bonds. To show the latter, we present an efficient algorithm for the newly defined pairwise local sequence-structure alignment (lssa) problem for RNA. For molecules of lengthes n and m, the algorithm has worst-case time complexity of O(n2 x m2 x max(n,m)) and a space complexity of only O(n x m). An implementation of our algorithm is available at http://www.bio.inf.uni-jena.de. Its runtime is competitive with global sequence-structure alignment.

Algorithms↗

PubMed2002

Constraint-based hydrophobic core construction for protein structure prediction in the face-centered-cubic lattice.

We present an algorithm for exact protein structure prediction in the FCC-HP-model. This model is a lattice protein model on the face-centered-cubic lattice that models the main force of protein folding, namely the hydrophobic force. The structure prediction for this model can be based on the construction of hydrophobic cores. The main focus of the paper is on an algorithm for constructing maximally and submaximally compact hydrophobic cores of a given size. This algorithm treats core construction as a constraint satisfaction problem (CSP), and the paper describes its constraint model. The algorithm employs symmetry excluding constraint-based search and relies heavily on good upper bounds on the number of contacts. Here, we use and strengthen upper bounds presented earlier. The resulting structure prediction algorithm (including previous work) handles sequences of sizes in the range of real proteins fast, i.e. we predict a first structure often within a few minutes. The algorithm is the first exact one for the FCC, besides full enumeration which is impracticable for chain lengths greater than about 15. We tested the algorithm successfully up to sequence length of 160, which is far beyond the capabilities even of previous heuristic approaches.

Algorithms↗