Biology subjects

Lonardi, S.

Publications and source records attributed to Lonardi, S..

28 records · Page 2Linked to original sources

bioRxiv2022

On the Prediction of non-CG DNA Methylation

DNA cytosine methylation is an epigenetic modification that has a critical role in gene regulation and genome stability. DNA methylation can be detected and measured using sequencing instruments after sodium bisulfite conversion, but experiments can be expensive for large eukaryotic genomes. Sequencing non-uniformity and mapping biases can leave parts of the genome with low or no coverage, thus hampering the ability of obtaining DNA methylation levels for all cytosines. To address these limitations, several computational methods have been proposed that can predict DNA methylation from the DNA sequence around the cytosine, or from the methylation level of nearby cytosines. Most of these methods are, however, entirely focused on CG methylation in humans and other mammals. In this work, we study for the first time the problem of predicting cytosine methylation for CG, CHG, and CHH contexts on five plant species, either from the DNA primary sequence around the cytosine or the methylation levels of neighboring cytosines. In this framework, we also study (1) the cross-species prediction problem, i.e., the classification performance when training on one species and testing on another species, and the (2) the cross-context prediction problem, i.e., the classification performance when training on one context and testing on another context (within the same species). Finally, we show that providing the classifier with gene annotation information allows our classifier to outperform the prediction accuracy of state-of-the-art methods.

bioinformatics↗

bioRxiv2021

Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica

Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in E. coli, use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeast Yarrowia lipolytica. A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuides ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that could be broadly applied to fungal species, prokaryotes, and other non-conventional organisms.

synthetic biology↗

bioRxiv2020

Mustache: Multi-scale Detection of Chromatin Loops from Hi-C and Micro-C Maps using Scale-Space Representation

We present MO_SCPLOWUSTACHEC_SCPLOW, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MO_SCPLOWUSTACHEC_SCPLOW employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in a multi-scale representation of chromatin contact maps parametrized by the size of the smoothing kernel. When applied to high-resolution Hi-C and Micro-C data, MO_SCPLOWUSTACHEC_SCPLOW detects loops at a wide range of genomic distances, identifying potential structural and regulatory interactions that are supported by independent conformation capture experiments as well as by known correlates of loop formation such as CTCF binding, enhancers and promoters. Unlike the commonly used HiCCUPS tool, MO_SCPLOWUSTACHEC_SCPLOW runs on general-purpose CPUs and it is very time efficient with a runtime of only a few minutes per chromosome for 5kb-resolution human genome contact maps. Extensive experimental results show that MO_SCPLOWUSTACHEC_SCPLOW reports two to three times the number of HiCCUPS loops, which are reproducible across replicates. It also recovers a larger proportion of published ChIA-PET and HiChIP loops than HiCCUPS. A comparative analysis of MO_SCPLOWUSTACHEC_SCPLOWs experimental results on Hi-C and Micro-C data confirms strong agreement between the two datasets with Micro-C providing better power for loop detection. Overall, our experimental results show that MO_SCPLOWUSTACHEC_SCPLOW enables a more efficient and comprehensive analysis of the chromatin looping from high-resolution Hi-C and Micro-C datasets. MO_SCPLOWUSTACHEC_SCPLOW is freely available at https://github.com/ay-lab/mustache.

bioinformatics↗

bioRxiv2019

DeeplyEssential: A Deep Neural Network for Predicting Essential Genes in Microbes

Essential genes are genes that critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. Here we propose a deep neural network (DNN) for predicting essential genes in microbes. Our DNN-based architecture called DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction, thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. Our extensive experimental results show that DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW outperforms existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. We also expose and study a hidden performance bias that affected previous classifiers.\n\nThe code of DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW is freely available at https://github.com/ucrbioinfo/DeeplyEssential

bioinformatics↗

bioRxiv2019

OMGS: Optical Map-based Genome Scaffolding

Due to the current limitations of sequencing technologies, de novo genome assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. While scaffolding is computationally easier than sequence assembly, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and inaccuracies in the linkage information. Genome scaffolding tools either use paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) as linkage information. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others). However, the most commonly used scaffolding tools have a serious limitation: they can only deal with one optical map at a time, forcing users to alternate or iterate over multiple maps. In this paper, we introduce a novel scaffolding algorithm called OMGS that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available, and produces comparable scaffolds using a single optical map. OMGS can be obtained from https://github.com/ucrbioinfo/OMGS

bioinformatics↗

bioRxiv2019

Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure

MotivationHigh-throughput conformation capture experiments such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods. Availabilityhttps://github.com/ucrbioinfo/Selfish Contactaroay001@ucr.edu and stelo@cs.ucr.edu

bioinformatics↗

bioRxiv2019

The genome of cowpea (Vigna unguiculata Walp.)

Cowpea (Vigna unguiculata [L.] Walp.) is a major crop for worldwide food and nutritional security, especially in sub-Saharan Africa, that is resilient to hot and drought-prone environments. A high-quality assembly of the single-haplotype inbred genome of cowpea IT97K-499-35 was developed by exploiting the synergies between single molecule real-time sequencing, optical and genetic mapping, and a novel assembly reconciliation algorithm. A total of 519 Mb is included in the assembled sequences. Nearly half of the assembled sequence is composed of repetitive elements, which are enriched within recombination-poor pericentromeric regions. A comparative analysis of these elements suggests that genome size differences between Vigna species are mainly attributable to changes in the amount of Gypsy retrotransposons. Conversely, genes are more abundant in more distal, high-recombination regions of the chromosomes; there appears to be more duplication of genes within the NBS-LRR and the SAUR-like auxin superfamilies compared to other warm-season legumes that have been sequenced. A surprising outcome of this study is the identification of a chromosomal inversion of 4.2 Mb among landraces and cultivars, which includes a gene that has been associated in other plants with interactions with the parasitic weed Striga gesnerioides. The genome sequence also facilitated the identification of a putative syntelog for multiple organ gigantism in legumes. A new numbering system has been adopted for cowpea chromosomes based on synteny with common bean (Phaseolus vulgaris).

genomics↗

bioRxiv2018

Validating genome-wide CRISPR-Cas9 function in the non-conventional yeast Yarrowia lipolytica

Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. To address this limitation, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which functional determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified mutations that led to high lipid accumulation and eliminated pseudohyphal morphology.

synthetic biology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗

bioRxiv2017

A multi-parent advanced generation inter-cross population for genetic analysis of multiple traits in cowpea (Vigna unguiculata L. Walp.)

Development and analysis of Multiparent Advanced Generation Inter-Cross (MAGIC) populations have been conducted with several crop plants to harness the potential for dissecting the genetic structure of traits and improving breeding populations. We developed a first MAGIC population for cowpea (Vigna unguiculata L. Walp.) from eight founder parents which are genetically diverse and carry many abiotic and biotic stress resistance, seed quality and agronomic traits relevant to cowpea improvement in sub-Saharan Africa (SSA) where cowpea is vitally important in the human diet and in local economies. The eight parents were inter-crossed using structured matings to ensure the population would have balanced representation from each of the founder parents, followed by single-seed descent, resulting in 365 F8 recombinant inbred lines (RILs) each carrying a mosaic of genome blocks contributed from all founders. This was confirmed by SNP genotyping with the cowpea Illumina 60K iSelect BeadArray. Following filtering to eliminate duplicates, sister lines and accidental selfing events, a core set of 305 F8 RILs was chosen as the primary population. The F8 lines were on average 99.74% homozygous while also diverse in agronomic traits including flowering time, growth habit, maturity, yield potential and seed characteristics across environments. Trait-associated SNPs were identified for most of the parental traits. Loci with major effects on photoperiod sensitivity and seed size were also verified by genetic mapping in biparental RIL populations. The distribution of recombination frequency varied considerably between chromosomes, with recombination hotspots distributed mostly in the telomeric regions. Due to its broad genetic base, this cowpea MAGIC population promises breakthroughs in genetic gain and high-resolution genetic mapping for gene discovery, enhancement of breeding populations and, for some lines, direct releases as new varieties.

genetics↗