High Read Coverage Research Articles

Selection in coastal Synechococcus (cyanobacteria) populations evaluated from environmental metagenomes.

Environmental metagenomics provides snippets of genomic sequences from all organisms in an environmental sample and are an unprecedented resource of information for investigating microbial population genetics. Current analytical methods, however, are poorly equipped to handle metagenomic data, particularly of short, unlinked sequences. A custom analytical pipeline was developed to calculate dN/dS ratios, a common metric to evaluate the role of selection in the evolution of a gene, from environmental metagenomes sequenced using 454 technology of flow-sorted populations of marine Synechococcus, the dominant cyanobacteria in coastal environments. The large majority of genes (98%) have evolved under purifying selection (dN/dS<1). The metagenome sequence coverage of the reference genomes was not uniform and genes that were highly represented in the environment (i.e. high read coverage) tended to be more evolutionarily conserved. Of the genes that may have evolved under positive selection (dN/dS>1), 77 out of 83 (93%) were hypothetical. Notable among annotated genes, ribosomal protein L35 appears to be under positive selection in one Synechococcus population. Other annotated genes, in particular a possible porin, a large-conductance mechanosensitive channel, an ATP binding component of an ABC transporter, and a homologue of a pilus retraction protein had regions of the gene with elevated dN/dS. With the increasing use of next-generation sequencing in metagenomic investigations of microbial diversity and ecology, analytical methods need to accommodate the peculiarities of these data streams. By developing a means to analyze population diversity data from these environmental metagenomes, we have provided the first insight into the role of selection in the evolution of Synechococcus, a globally significant primary producer.

Translational Database Selection and Multiplexed Sequence Capture for Up Front Filtering of Reliable Breast Cancer Biomarker Candidates

Biomarker identification is of utmost importance for the development of novel diagnostics and therapeutics. Here we make use of a translational database selection strategy, utilizing data from the Human Protein Atlas (HPA) on differentially expressed protein patterns in healthy and breast cancer tissues as a means to filter out potential biomarkers for underlying genetic causatives of the disease. DNA was isolated from ten breast cancer biopsies, and the protein coding and flanking non-coding genomic regions corresponding to the selected proteins were extracted in a multiplexed format from the samples using a single DNA sequence capture array. Deep sequencing revealed an even enrichment of the multiplexed samples and a great variation of genetic alterations in the tumors of the sampled individuals. Benefiting from the upstream filtering method, the final set of biomarker candidates could be completely verified through bidirectional Sanger sequencing, revealing a 40 percent false positive rate despite high read coverage. Of the variants encountered in translated regions, nine novel non-synonymous variations were identified and verified, two of which were present in more than one of the ten tumor samples.

Detecting copy number aberrations (CNA) in cancer genomes using high-throughput sequencing technologies

Abstract The karyotype of human tumors are often aneuploid: besides these numerical deviations, there are often structural rearrangements within individual chromosomes such as amplifications, deletions and translocations. In the resulting genome, profound and complex alterations in the underlying gene network and dosage occur, giving rise to the observed malignant phenotype. To understand how these events contribute to the biology of a tumor cell, a first crucial step is to be able to detect variation in chromosomal structure and, in particular, copy number. The recent development in technology, such as array comparative genomic hybridization (aCGH), SNP microarray and, more recently, high throughput sequencing, made it feasible to detect copy number aberrations (CNA). Using these techniques it is possible to obtain, for thousands of regions across the genome, a numerical value proportional to the chromosome copy number for each of the accessed regions. Comparing DNA from tumor to normal samples, it is possible to identify copy number aberrations across the genome. However, the methodology used to calculate CNA from the raw data usually makes one or both of the following assumptions: • The starting material consists of genetically homogeneous cells. • The overall size of a tumor genome is very similar to the size of the normal genome. The first assumption is completely reasonable when dealing with cell lines, but the same cannot be said when the DNA analyzed is isolated from patients’ tumors. Infiltrations with stromal or endothelial cells are, in fact, largely inevitable. The second assumption, that has a strong impact on the normalization step, is often not correct. Given the scale, extent and severity of chromosomal structural changes, the overall genetic material of a cancer cell might be significantly different (usually larger) than a normal one. Therefore, assuming that the total size of a cancer genome is comparable to the size of the normal genome, might lead to artifacts and misleading results. Some methods do not strictly require assumptions on the size of the genome, but they rely on the ability to detect SNP variants and distinguish between the two alleles of a heterozygous region. They can then infer when two, three, four or more copies are present. Although high throughput sequencing could also detect SNP variants, a very high read coverage would be required, making the technology far too expensive at present. Here, we propose a method to obtain CNA from high throughput sequencing that avoids the two assumptions mentioned above. The method counts the number of reads mapped to a region of fixed length both in tumor and normal DNAs from the same patient. For each region, the ratio between number of reads from the tumor sample and the normal sample depends mainly on three factors: • The average number of copies (including stromal contamination) of a given chromosomal region in the two samples. • The overall depth of coverage (total number of reads) of the two samples. • Sampling error. The goal is to detect, for each chromosomal region, the underlying ratio between number of copies in the tumor genome versus the normal genome, despite the noise due to different depth of coverage and the sampling error. For each chromosomal region, we first calculate the ratio of read counts between tumor and normal. This way we balance out various biases due to biological and technical issues (i.e. GC content, aligning artifacts). Second, to reduce the sampling error with minimal effect on the resolution, we use a segmentation algorithm on the obtained ratio. At this point, we look at the distribution of ratio in all chromosomal regions and we fit a model of several normal distributions with equally spaced means. The ratio from each region can thus be assigned to one of these distributions and underlying CNA estimated. We are testing the algorithm on a series of simulated and real samples to access the strength of the proposed method.

Aggressive assembly of pyrosequencing reads with mates

Motivation: DNA sequence reads from Sanger and pyrosequencing platforms differ in cost, accuracy, typical coverage, average read length and the variety of available paired-end protocols. Both read types can complement one another in a ‘hybrid’ approach to whole-genome shotgun sequencing projects, but assembly software must be modified to accommodate their different characteristics. This is true even of pyrosequencing mated and unmated read combinations. Without special modifications, assemblers tuned for homogeneous sequence data may perform poorly on hybrid data.Results: Celera Assembler was modified for combinations of ABI 3730 and 454 FLX reads. The revised pipeline called CABOG (Celera Assembler with the Best Overlap Graph) is robust to homopolymer run length uncertainty, high read coverage and heterogeneous read lengths. In tests on four genomes, it generated the longest contigs among all assemblers tested. It exploited the mate constraints provided by paired-end reads from either platform to build larger contigs and scaffolds, which were validated by comparison to a finished reference sequence. A low rate of contig mis-assembly was detected in some CABOG assemblies, but this was reduced in the presence of sufficient mate pair data.Availability: The software is freely available as open-source from http://wgs-assembler.sf.net under the GNU Public License.Contact: jmiller@jcvi.orgSupplementary information: Supplementary data are available at Bioinformatics online.