Biology subjects

Rausell, A.

Publications and source records attributed to Rausell, A..

3 recordsLinked to original sources

bioRxiv2020

Cell-ID: gene signature extraction and cell identity recognition at individual cell level

The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell sequencing has made it necessary to use clustering-based computational approaches in which the characterization of cell-type heterogeneity is performed at cell-subpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. Cell-ID signatures allow unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics technologies. Cell-ID is distributed as an open-source R software package: https://github.com/RausellLab/CelliD.

bioinformatics↗

bioRxiv2019

Common homozygosity for predicted loss-of-function variants reveals both redundant and advantageous effects of dispensable human genes

Humans homozygous or hemizygous for variants predicted to cause a loss of function of the corresponding protein do not necessarily present with overt clinical phenotypes. However, the set of effectively dispensable genes in the human genome has not yet been fully characterized. We report here 190 autosomal genes with 207 predicted loss-of-function variants, for which the frequency of homozygous individuals exceeds 1% in at least one human population from five major ancestry groups. No such genes were identified on the X and Y chromosomes. Manual curation revealed that 28 variants (15%) had been misannotated as loss-of-function, mainly due to linkage disequilibrium with different compensatory variants. Of the 179 remaining variants in 166 genes (0.82% of 20,232 genes), only 11 alleles in 11 genes had previously been confirmed experimentally to be loss-of-function. The set of 166 dispensable genes was enriched in olfactory receptor genes (41 genes), but depleted of genes expressed in a wide range of organs and in leukocytes. The 125 dispensable non-olfactory receptor genes displayed a relaxation of selective constraints both between species and within humans, consistent with greater redundancy. In total, 62 of these 125 genes were found to be dispensable in at least three human populations, suggesting possible evolution toward pseudogenes. Out of the 179 common loss-of-function variants, 72 could be tested for two neutrality selection statistics, and eight displayed robust signals of positive selection. These variants included the known FUT2 mutant allele conferring resistance to intestinal viruses and an APOL3 variant involved in resistance to parasitic infections. Finally, the 41 dispensable olfactory receptor genes also displayed a strong relaxation of selective constraints similar to that observed for the 341 non-dispensable olfactory receptor genes. Overall, the identification of 166 genes for which a sizeable proportion of humans are homozygous for predicted loss-of-function alleles reveals both redundancies and advantages of such deficiencies for human survival.\n\nSignificance statementHuman genes homozygous for seemingly loss of function (LoF) variants are increasingly reported in a sizeable proportion of individuals without overt clinical phenotypes. Here, we found 166 genes with 179 predicted LoF variants for which the frequency of homozygous individuals exceeds 1% in at least one of the populations present in databases ExAC and gnomAD. This set of putatively dispensable genes showed relaxation of selective constraints suggesting that a large number of these genes are undergoing pseudogenization. Eight of the common LoF variants displayed robust signals of positive selection including two variants located in genes involved in resistance to infectious diseases. The identification of dispensable genes will allow identifying functions that are, at least nowadays, redundant, or possibly advantageous, for human survival.

genetics↗

bioRxiv2018

Scoring of pathogenic non-coding variants in Mendelian diseases through supervised learning on ancient, recent and ongoing purifying selection signals in human

The study of rare Mendelian diseases through exome sequencing typically yields incomplete diagnostic rates, ~8-70% depending on the disease type. Whole genome sequencing of the unresolved cases allows addressing the hypothesis that causal variants could lay in non-coding regions with damaging regulatory consequences. The large amount of rare and singleton variants found in each individual genome requires computational filtering and scoring strategies to gain power in downstream statistical genetics tests. However, state-of-the-art methods estimating the functional relevance of non-coding genomic regions have been mostly characterized on sets of variants largely composed of trait-associated polymorphisms and associated to common diseases, yet with modest accuracy and strong positional biases. In this work we first curated a collection of n=737 high-confidence pathogenic non-coding single-nucleotide variants in proximal cis-regulatory genomic regions associated to monogenic Mendelian diseases. We then systematically evaluated the ability to predict causal variants of a comprehensive set of natural selection features extracted at three genomic levels: the affected position, the flanking region and the associated gene. In addition to inter-species conservation, a comprehensive set of recent and ongoing purifying selection signals in human was explored, allowing to capture potential constraints associated to recently acquired regulatory elements in the human lineage. A supervised learning approach using gradient tree boosting on such features reached a high predictive performance characterized by an area under the ROC curve = 0.84 and an area under the Precision-Recall curve = 0.47. The figures represent a relative improvement of >10% and >34% respectively upon the performance of current state-of-the-art methods for prioritizing non-coding variants. Performance was consistent under multiple configurations of the sets of variants used for learning and for independent testing. The supervised learning design allowed the assessment of newly seen non-coding variants overcoming gene and positional bias. The scores produced by the approach allow a more consistent weighting and aggregation of candidate pathogenic variants from diverse non-coding regions within and across genes in the context of statistical tests for rare variant association analysis.

bioinformatics↗