Biology subjects

Notredame, C.

Publications and source records attributed to Notredame, C..

13 recordsLinked to original sources

bioRxiv2025

FAMSA2 enables accurate multiple sequence alignment at protein-universe scale

We introduce FAMSA2, an algorithm that produces high-accuracy multiple protein sequence alignments with unprecedented speed. Across structural, phylogenetic, and functional benchmarks, FAMSA2 matches or exceeds the accuracy of state-of-the-art tools while running up to 400 times faster and outperforming the fastest existing aligners. The ability of FAMSA2 to process 12 million sequences in 40 minutes on a 64 GB RAM workstation enables evolutionary and structural analyses at previously unattainable scales.

bioinformatics↗

bioRxiv2025

Evidence of distal regulations orchestrated by RNAs initiating at short tandem repeats

Short Tandem Repeats (STRs), also called microsatellites, correspond to tandemly repeated short DNA motifs (1 to 6 bp) and are one of the most polymorphic and abundant repetitive elements in the human genome. Variations of their length (i.e. number of consecutive repeats) have been implicated in gene expression regulation (termed expression(e)STRs). Using captrapping followed by long read sequencing, we discovered that STRs can host transcription start sites, the presence of which depends mainly on STR flanking sequences. Here, we investigate the effect of SNPs located in these sequences and ask whether STR-initiating RNAs have regulatory potential. First, we develop fully interpretable deep learning-based models, called Modular Neural Networks, able to predict, for each STR class, the level of RNAs using 101bp-long sequences encompassing STRs. Analysis of MNN filters allows us to identify multiple regulatory elements and candidate transcription factors. Second, leveraging genome sequencing and gene expression data from the Genotype-Tissue Expression project, we use the output of our models to link the levels of STR-initiating RNAs to the expression of nearby genes. We identify 14,340 significant associations (coined RNA(r)STRs) and illustrate how this novel resource can help interpret non-coding variants associated with complex traits and diseases. Third, we unveil an intricate transcriptional interplay between STR-initiating RNAs and Alu repeats that may couple their regulatory actions, extending both the nature and the functional importance of non-coding transcription and shedding new light on the complexity of distal regulations orchestrated by repeated sequences.

genomics↗

bioRxiv2025

An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study

The computational complexity of many key bioinformatics problems has resulted in numerous alternative heuristic solutions, where no single approach consistently outperforms all others. This creates difficulties for users trying to identify the most suitable tool for their dataset and for developers managing and evaluating alternative methods. As data volumes grow, deploying these methods becomes increasingly difficult, highlighting the need for standardized frameworks for seamless tool deployment and comparison in HPC environments. Multiple sequence aligners (MSAs) rank among the most commonly employed modeling techniques in bioinformatics, playing a crucial role in applications such as protein structure prediction, phylogenetic reconstruction, and variant effect prediction. The NP-hardness of MSAs makes them a major example of problems where heuristics stand central, as no optimal solution can be currently obtained, within the limits of operational computational requirements. Here, we present a pilot design of an nf-core framework for streamlined tool deployment and rigorous performance evaluation focusing on the MSAs software ecosystem. By integrating the most popular MSA tools and focusing on a modular, and extensible architecture, we aspire to provide a key platform supporting MSA deployment, evaluation, and algorithmics development to the MSA community, and a proof-of-principle to the wider bioinformatics community.

bioinformatics↗

bioRxiv2024

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs. We reconstruct these motifs with a new computational platform -- Joint Protein-Ligand Embedding (JPLE) -- which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of known RBP motifs, expanding the motif repertoire across all major eukaryotic clades, and assigning motifs to the majority of human RBPs. EuPRI drastically improves knowledge of RBP motifs in flowering plants. For example, it increases the number of Arabidopsis thaliana RBP motifs 7-fold, from 14 to 105. EuPRI also has broad utility for inferring post-transcriptional function and evolutionary relationships. We demonstrate this by predicting a role for 12 Arabidopsis thaliana RBPs in RNA stability and identifying rapid and recent evolution of post-transcriptional regulatory networks in worms and plants. In contrast, the vertebrate RNA motif set has remained relatively stable after its drastic expansion between the metazoan and vertebrate ancestors. EuPRI represents a powerful resource for the study of gene regulation across eukaryotes.

genomics↗

bioRxiv2022

Evolutionary insights reveal a new role of PADI2 in transcriptional elongation

Peptidyl arginine deiminases (PADIs) catalyze protein citrullination, a post-translational conversion of arginine to citrulline. The most widely expressed member of this family, PADI2, regulates cellular processes that impact several diseases. We hypothesized that we could gain new insights into PADI2 function through a systematic evolutionary and structural analysis. Here, we identify 20 positively selected PADI2 residues, 16 of which are structurally exposed and maintain PADI2 interactions with cognate proteins. Many of these selected residues reside in non-catalytic regions of PADI2. We validate the importance of a prominent loop in the middle domain that encompasses PADI2 L162, a residue under positive selection. This site is essential for interaction with the transcription elongation factor (P-TEFb) and mediates active transcription of the oncogenes c-MYC, and CCNB1, as well as impacting cellular proliferation. These insights could be key to understanding and addressing the role of the PADI2 c-MYC axis in cancer progression. Significance StatementHere we use a systematic evolutionary analysis to identify positively selected residues in the non-catalytic domain of PADI2 and link the positive selection of key residues to a role in transcription. Specifically, a structurally exposed loop in the PADI2 middle domain encompasses the positively selected residue L162 which is linked to transcription and cellular proliferation. This loop contributes to PADI2 interaction with the P-TEFb complex and cellular proliferation. Our results showcase the utility of combining evolutionary and experimental approaches to dissect the evolution of essential functional processes.

evolutionary biology↗

bioRxiv2022

Improving sequence alignments with AlphaFold2 regardless of structural modeling accuracy

Protein sequence alignments are essential to structural, evolutionary and functional analysis but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly superior to sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis.

bioinformatics↗

bioRxiv2021

Four layer multi-omics reveals molecular responses to aneuploidy in Leishmania

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, protein complex subunits and non-cytoplasmic proteins, showed dosage compensation, responding less or even not at all to aneuploidy-induced dosage changes. In contrast to other Eukaryotes, we did not observe the widespread regulation at the transcript level that typically modulates some of the negative effects of aneuploidy. Further, the majority of differentially expressed proteins between aneuploid strains were encoded by non-aneuploid chromosomes and were not driven by a significant underlying transcript change, suggesting that aneuploidy is accompanied by extensive post-transcriptional protein-level modulation. This makes Leishmania a unique Eukaryotic model for elucidating post-transcriptional protein-abundance modulation in the context of aneuploidy.

systems biology↗

bioRxiv2020

A long non-coding RNA controls parasite differentiation in African trypanosomes

Trypanosoma brucei causes African sleeping sickness, a fatal human disease. Its differentiation from replicative slender form into quiescent stumpy form promotes host survival and parasite transmission. Long noncoding RNAs (lncRNAs) are known to regulate cell differentiation. To determine whether lncRNAs are involved in parasite differentiation we used RNAseq to survey the T. brucei lncRNA gene repertoire, identifying 1,428 previously uncharacterized lncRNA genes. We analysed grumpy, a lncRNA located immediately upstream of an RNA-binding protein that is a key differentiation regulator. Grumpy over-expression resulted in premature parasite differentiation into the quiescent stumpy form, and subsequent impairment of in vivo infection, decreasing parasite load in the mammalian host, and increasing host survival. Our analyses suggest Grumpy is one of many lncRNA that modulate parasite-host interactions, and lncRNA roles in cell differentiation are probably commonplace in T. brucei.

microbiology↗

bioRxiv2018

Fast and accurate large multiple sequence alignments using root-to-leave regressive computation

Inferences derived from large multiple alignments of biological sequences are critical to many areas of biology, including evolution, genomics, biochemistry, and structural biology. However, the complexity of the alignment problem imposes the use of approximate solutions. The most common is the progressive algorithm, which starts by aligning the most similar sequences, incorporating the remaining ones following the order imposed by a guide-tree. We developed and validated on protein sequences a regressive algorithm that works the other way around, aligning first the most dissimilar sequences. Our algorithm produces more accurate alignments than non-regressive methods, especially on datasets larger than 10,000 sequences. By design, it can run any existing alignment method in linear time thus allowing the scale-up required for extremely large genomic analyses.\n\nOne Sentence SummaryInitiating alignments with the most dissimilar sequences allows slow and accurate methods to be used on large datasets

bioinformatics↗

bioRxiv2018

A field guide for the compositional analysis of any-omics data

Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. Today, NGS is routinely used to understand many important topics in biology from human disease to microorganism diversity. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: the magnitude of the counts are determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged, and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when comparing heterogeneous samples (e.g., samples collected across distinct cancers or tissues). Instead, methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. In this manuscript, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. In doing so, we review zero replacement, differential abundance analysis, and within-group and between-group coordination analysis. We then discuss how this pipeline can accommodate complex study design, facilitate the analysis of vertically and horizontally integrated data, including multiomics data, and further extend to single-cell sequencing data. In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, \"Relative to some important activity of the cell, what is changing?\". Taken together, this manuscript establishes the first fully comprehensive analysis protocol that is suitable for any and all -omics data.

bioinformatics↗

bioRxiv2017

Lessons Learned: Recommendations for Establishing Critical Periodic Scientific Benchmarking

The dependence of life scientists on software has steadily grown in recent years. For many tasks, researchers have to decide which of the available bioinformatics software are more suitable for their specific needs. Additionally researchers should be able to objectively select the software that provides the highest accuracy, the best efficiency and the highest level of reproducibility when integrated in their research projects.\n\nCritical benchmarking of bioinformatics methods, tools and web services is therefore an essential community service, as well as a critical component of reproducibility efforts. Unbiased and objective evaluations are challenging to set up and can only be effective when built and implemented around community driven efforts, as demonstrated by the many ongoing community challenges in bioinformatics that followed the success of CASP. Community challenges bring the combined benefits of intense collaboration, transparency and standard harmonization. Only open systems for the continuous evaluation of methods offer a perfect complement to community challenges, offering to larger communities of users that could extend far beyond the community of developers, a window to the developments status that they can use for their specific projects. We understand by continuous evaluation systems as those services which are always available and periodically update their data and/or metrics according to a predefined schedule keeping in mind that the performance has to be always seen in terms of each research domain.\n\nWe argue here that technology is now mature to bring community driven benchmarking efforts to a higher level that should allow effective interoperability of benchmarks across related methods. New technological developments allow overcoming the limitations of the first experiences on online benchmarking e.g. EVA. We therefore describe OpenEBench, a novel infra-structure designed to establish a continuous automated benchmarking system for bioinformatics methods, tools and web services.\n\nOpenEBench is being developed so as to cater for the needs of the bioinformatics community, especially software developers who need an objective and quantitative way to inform their decisions as well as the larger community of end-users, in their search for unbiased and up-to-date evaluation of bioinformatics methods. As such OpenEBench should soon become a central place for bioinformatics software developers, community-driven benchmarking initiatives, researchers using bioinformatics methods, and funders interested in the result of methods evaluation.

bioinformatics↗

bioRxiv2017

Differential Proportionality - A Normalization-Free Approach To Differential Gene Expression

Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

bioinformatics↗

bioRxiv2017

Evolutionary footprints reveal insights into plant microRNA biogenesis

MicroRNAs (miRNAs) are endogenous small RNAs that recognize target sequences by base complementarity. They are processed from longer precursors that harbor a fold-back structure. Plant miRNA precursors are quite variable in size and shape, and are recognized by the processing machinery in different ways. However, ancient miRNAs and their binding sites in target genes are conserved during evolution. Here, we designed a strategy to systematically analyze MIRNAs from different species generating a graphical representation of the conservation of the primary sequence and secondary structure. We found that plant MIRNAs have evolutionary footprints that go beyond the small RNA sequence itself, yet, their location along the precursor depends on the specific MIRNA. We show that these conserved regions correspond to structural determinants recognized during the biogenesis of plant miRNAs. Furthermore, we found that the members of the miR166 family have unusual conservation patterns and demonstrated that the recognition of these precursors in vivo differs from other known miRNAs. Our results describe a link between the evolutionary conservation of plant MIRNAs and the mechanisms underlying the biogenesis of these small RNAs, and show that the MIRNA pattern of conservation can be used to infer the mode of miRNA biogenesis.

plant biology↗