Biology subjects

Stefano Lonardi

Publications and source records attributed to Stefano Lonardi.

4 recordsLinked to original sources

bioRxiv2016

Genome resources for climate-resilient cowpea, an essential crop for food security

Cowpea (Vigna unguiculata L. Walp.) is a legume crop that is resilient to hot and drought-prone climates, and a primary source of protein in sub-Saharan Africa and other parts of the developing world. However, genome resources for cowpea have lagged behind most other major crop plants. Here we describe foundational genome resources and their application to analysis of germplasm currently in use in West African breeding programs. Resources developed from the African cultivar IT97K-499-35 include bacterial artificial chromosome (BAC) libraries and a BAC-based physical map, assembled sequences from 4,355 BACs, as well as a whole-genome shotgun (WGS) assembly. These resources and WGS sequences of an additional 36 diverse cowpea accessions supported the development of a genotyping assay for over 50,000 SNPs, which was then applied to five biparental RIL populations to produce a consensus genetic map containing 37,372 SNPs. This genetic map enabled the anchoring of 100 Mb of WGS and 420 Mb of BAC sequences, an exploration of genetic diversity along each linkage group, and clarification of macrosynteny between cowpea and common bean. The genomes of West African breeding lines and landraces have regions of marked depletion of diversity, some of which coincide with QTL that may be the result of artificial selection or environmental adaptation. The new publicly available resources and knowledge help to define goals and accelerate the breeding of improved varieties to address food security issues related to limited-input small-holder farming and climate stress.

Genomics↗

bioRxiv2016

Higher classification sensitivity of short metagenomic reads with CLARK-S

The growing number of metagenomic studies in medicine and environmental sciences is creating increasing demands on the computational infrastructure designed to analyze these very large datasets. Often, the construction of ultra-fast and precise taxonomic classifiers can compromise on their sensitivity (i.e., the number of reads correctly classified). Here we introduce CLARK-S, a new software tool that can classify short reads with high precision, high sensitivity and high speed at the same time.

Bioinformatics↗

bioRxiv2015

Sequencing of 15,622 gene-bearing BACs reveals new features of the barley genome

Barley (Hordeum vulgare L.) possesses a large and highly repetitive genome of 5.1 Gb that has hindered the development of a complete sequence. In 2012, the International Barley Sequencing Consortium released a resource integrating whole-genome shotgun sequences with a physical and genetic framework. However, since only 6,278 BACs in the physical map were sequenced, detailed fine structure was limited. To gain access to the gene-containing portion of the barley genome at high resolution, we identified and sequenced 15,622 BACs representing the minimal tiling path of 72,052 physical mapped gene-bearing BACs. This generated about 1.7 Gb of genomic sequence containing 17,386 annotated barley genes. Exploration of the sequenced BACs revealed that although distal ends of chromosomes contain most of the gene-enriched BACs and are characterized by high rates of recombination, there are also gene-dense regions with suppressed recombination. Knowledge of these deviant regions is relevant to trait introgression, genome-wide association studies, genomic selection model development and map-based cloning strategies. Sequences and their gene and SNP annotations can be accessed and exported via http://harvest-web.org/hweb/utilmenu.wc or through the software HarvEST:Barley (download from harvest.ucr.edu). In the latter, we have implemented a synteny viewer between barley and Aegilops tauschii to aid in comparative genome analysis.

Genomics↗

bioRxiv2015

When Less is More: "Slicing" Sequencing Data Improves Read Decoding Accuracy and De Novo Assembly Quality

Since the invention of DNA sequencing in the seventies, computational biologists have had to deal with the problem de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, for the first time we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing. Specifically, we explore the effect of ultra-deep sequencing data in two domains: (i) the problem of decoding reads to BAC clones (in the context of the combinatorial pooling design proposed in [1]), and (ii) the problem of de novo assembly of BAC clones. Using real ultra-deep sequencing data, we show that when the depth of sequencing increases over a certain threshold, sequencing errors make these two problems harder and harder (instead of easier, as one would expect with error-free data), and as a consequence the quality of the solution degrades with more and more data. For the first problem, we propose an effective solution based on \"divide and conquer\": we \"slice\" a large dataset into smaller samples of optimal size, decode each slice independently, then merge the results. Experimental results on over 15,000 barley BACs and over 4,000 cowpea BACs demonstrate a significant improvement in the quality of the decoding and the final assembly. For the second problem, we show for the first time that modern de novo assemblers cannot take advantage of ultra-deep sequencing data.

Bioinformatics↗