Biology subjects

Erb, I.

Publications and source records attributed to Erb, I..

10 recordsLinked to original sources

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

Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

In the health sciences, many data sets produced by next-generation sequencing (NGS) only contain relative information because of biological and technical factors that limit the total number of nucleotides observed for a given sample. As mutually dependent elements, it is not possible to interpret any component in isolation, at least without normalization. The field of compositional data analysis (CoDA) has emerged with alternative methods for relative data based on log-ratio transforms. However, NGS data often contain many more features than samples, and thus require creative new ways to reduce the dimensionality of the data without sacrificing interpretability. The summation of parts, called amalgamation, is a practical way of reducing dimensionality, but can introduce a non-linear distortion to the data. We exploit this non-linearity to propose a powerful yet interpretable dimension reduction method. In this report, we present data-driven amalgamation as a new method and conceptual framework for reducing the dimensionality of compositional data. Unlike expert-driven amalgamation which requires prior domain knowledge, our data-driven amalgamation method uses a genetic algorithm to answer the question, "What is the best way to amalgamate the data to achieve the user-defined objective?". We present a user-friendly R package, called amalgam, that can quickly find the optimal amalgamation to (a) preserve the distance between samples, or (b) classify samples as diseased or not. Our benchmark on 13 real data sets confirm that these amalgamations compete with the state-of-the-art unsupervised and supervised dimension reduction methods in terms of performance, but result in new variables that are much easier to understand: they are groups of features added together.

bioinformatics↗

bioRxiv2019

Another look at microbe-metabolite interactions: how scale invariant correlations can outperform a neural network

Many scientists are now interested in studying the correlative relationships between microbes and metabolites. However, these kinds of analyses are complicated by the compositional (i.e., relative) nature of the data. Recently, Morton et al. proposed a neural network architecture called mmvec to predict metabolite abundances from microbe presence. They introduce this method as a scale invariant solution to the integration of multi-omics compositional data, and claim that "mmvec is the only method robust to scale deviations". We do not doubt the utility of mmvec, but write in defense of simple linear statistics. In fact, when used correctly, correlation and proportionality can actually outperform the mmvec neural network.

bioinformatics↗

bioRxiv2019

Using balances to engineer features for the classification of health biomarkers: a new approach to balance selection

Since the turn of the century, technological advances have made it possible to obtain a molecular profile of any tissue in a cost-effective manner. Among these advances include sophisticated high-throughput assays that measure the relative abundance of microorganisms, RNA molecules, and metabolites. While these data are most often collected to gain new insights into biological systems, they can also be used as biomarkers to create clinically useful diagnostic classifiers. How best to classify high-dimensional \"-omics\" data remains an area of active research. However, few explicitly model the relative nature of these data, and instead rely on cumbersome normalizations which often invoke untestable assumptions. This report (a) emphasizes the relative nature of health biomarkers, (b) discusses the literature surrounding the classification of relative data, and (c) benchmarks how different transformations perform across multiple biomarker types. In doing so, this report explores how one could use balances to engineer features prior to classification, and proposes a simple procedure, called discriminative balance analysis, to select discriminative 2- and 3-part balances.

bioinformatics↗

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↗

bioRxiv2018

Impaired development of neocortical circuits contributes to the neurological alterations in DYRK1A haploinsufficiency syndrome

Autism spectrum disorders are early onset neurodevelopmental disorders characterized by deficits in social communication and restricted repetitive behaviors, yet they are quite heterogeneous in terms of their genetic basis and phenotypic manifestations. Recently, de novo pathogenic mutations in DYRK1A, a chromosome 21 gene associated to neuropathological traits of Down syndrome, have been identified in patients presenting a recognizable syndrome included in the autism spectrum. These mutations produce DYRK1A kinases with partial or complete absence of the catalytic domain, or they represent missense mutations located within this domain. Here, we undertook an extensive biochemical characterization of the DYRK1A missense mutations reported to date and show that most of them, but not all, result in enzymatically dead DYRK1A proteins. We also show that haploinsufficient Dyrk1a+/- mutant mice mirror the neurological traits associated with the human pathology, such as defective social interactions, stereotypic behaviors and epileptic activity. These mutant mice present altered proportions of excitatory and inhibitory neocortical neurons and synapses. Moreover, we provide evidence that alterations in the production of cortical excitatory neurons are contributing to these defects. Indeed, by the end of the neurogenic period, the expression of developmental regulated genes involved in neuron differentiation and/or activity is altered. Therefore, our data indicate that altered neocortical neurogenesis could critically affect the formation of cortical circuits, thereby contributing to the neuropathological changes in DYRK1A haploinsufficiency syndrome.

neuroscience↗

bioRxiv2017

Understanding sequencing data as compositions: anoutlook and review

MotivationAlthough seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models.\n\nResultsThe purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

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↗