基于短序列测序数据的四倍体拟南芥转录组研究

为了促进对四倍体拟南芥(A.suecica)的研究,阐明多倍体植物在染色体加倍过程中遗传物质的变化,从而在分子层面上解释多倍体植物的环境适应和进化机制,描述了一套基于第二代测序技术的转录组短序列组装和生物信息学分析方法.通过对23 000 000条来至于Illumina测序平台的序列数据进行SOAPdenovo组装,以...     

Exploring drought stress-regulated genes in senna (Cassia angustifolia Vahl.): a transcriptomic approach

Functional & Integrative Genomics - De novo assembly of reads produced by next-generation sequencing (NGS) technologies offers a rapid approach to obtain expressed gene sequences for non-model...     

Scaffolding pre-assembled contigs using SSPACE

SUMMARY: De novo assembly tools play a main role in reconstructing genomes from next-generation sequencing (NGS) data and usually yield a number of contigs. Using paired-read sequencing data it is possible to assess the order, distance and orientation of contigs and combine them into so-called scaffolds. Although the latter process is a crucial step in finishing genomes, scaffolding algorithms are often built-in functions in de novo assembly tools and cannot be independently controlled. We here present a new tool, called SSPACE, which is a stand-alone scaffolder of pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads. SSPACE shows promising results on both prokaryote and eukaryote genomic testsets where the amount of initial contigs was reduced by at least 75%.     

HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations

Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.     

Evaluating the fidelity of de novo short read metagenomic assembly using simulated data

A frequent step in metagenomic data analysis comprises the assembly of the sequenced reads. Many assembly tools have been published in the last years targeting data coming from next-generation sequencing (NGS) technologies but these assemblers have not been designed for or tested in multi-genome scenarios that characterize metagenomic studies. Here we provide a critical assessment of current de novo short reads assembly tools in multi-genome scenarios using complex simulated metagenomic data. With this approach we tested the fidelity of different assemblers in metagenomic studies demonstrating that even under the simplest compositions the number of chimeric contigs involving different species is noticeable. We further showed that the assembly process reduces the accuracy of the functional classification of the metagenomic data and that these errors can be overcome raising the coverage of the studied metagenome. The results presented here highlight the particular difficulties that de novo genome assemblers face in multi-genome scenarios demonstrating that these difficulties, that often compromise the functional classification of the analyzed data, can be overcome with a high sequencing effort.