Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher’s exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment

We previously described the whole-genome assembly program Arachne, presenting assemblies of simulated data for small to mid-sized genomes. Here we describe algorithmic adaptations to the program, allowing for assembly of mammalian-size genomes, and also improving the assembly of smaller genomes. Three principal changes were simultaneously made and applied to the assembly of the mouse genome, during a six-month period of development: (1) Supercontigs (scaffolds) were iteratively broken and rejoined using several criteria, yielding a 64-fold increase in length (N50), and apparent elimination of all global misjoins; (2) gaps between contigs in supercontigs were filled (partially or completely) by insertion of reads, as suggested by pairing within the supercontig, increasing the N50 contig length by 50%; (3) memory usage was reduced fourfold. The outcome of this mouse assembly and its analysis are described in (Mouse Genome Sequencing Consortium 2002). In the whole-genome shotgun method of genome sequencing, as presently practiced, the entire genome is sheared to specified approximate sizes (generally chosen from the range 2–200 kb), yielding random fragments (called inserts), whose ends can then be determined, yielding sequence reads of size about 600 bp, given in pairs whose genomic separation is approximately known from the insert size (Sanger et al. 1977; Edwards et al. 1990). In principle, the genome can then be reconstructed in silico from these reads, using their overlap and pairing as glue, provided that the inserts gave sufficiently even representation of the genome, that an appropriate mix of insert lengths (short to long)was used, and that the DNA itself was sufficiently free of polymorphism (Sanger et al.

Recent advances in DNA sequencing technology and their focal role in Genome Wide Association Studies (GWAS) have rekindled a growing interest in the whole-genome sequence assembly (WGSA) problem, thereby, inundating the field with a plethora of new formalizations, algorithms, heuristics and implementations. And yet, scant attention has been paid to comparative assessments of these assemblers ’ quality and accuracy. No commonly accepted and standardized method for comparison exists yet. Even worse, widely used metrics to compare the assembled sequences emphasize only size, poorly capturing the contig quality and accuracy. This paper addresses these concerns: it highlights common anomalies in assembly accuracy through a rigorous study of several assemblers, compared under both standard metrics (N50, coverage, contig sizes, etc.) as well as a more comprehensive metric (Feature-Response Curves, FRC) that is introduced here; FRC transparently captures the trade-offs between contigs ’ quality against their sizes. For this purpose, most of the publicly available major sequence assemblers – both for low-coverage long (Sanger) and high-coverage short (Illumina) reads technologies – are compared. These assemblers are applied to microbial (Escherichia coli, Brucella, Wolbachia, Staphylococcus, Helicobacter) and partial human genome sequences (Chr. Y), using sequence reads of various readlengths, coverages, accuracies, and with and without mate-pairs. It is hoped that, based on these evaluations, computational biologists will identify innovative sequence assembly paradigms, bioinformaticists will determine promising

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In Kellis et al. (2003), we reported the genome sequences of S. paradoxus, S. mikatae, and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genomewide comparative analysis allowed the identification of functionally important sequences, both coding and noncoding. In this companion paper we describe the mathematical and algorithmic results underpinning the analysis of these genomes. (1) We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90 % of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change. (2) We present methods for the identification of proteincoding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10 % of previously

In recent years a flurry of new DNA sequencing technologies have altered the landscape of genomics, providing a vast amount of sequence information at a fraction of the costs that were previously feasible. The task of assembling these sequences into a genome has, however, still remained an algorithmic challenge that is in practice answered by heuristic solutions. In order to design better assembly algorithms and exploit the characteristics of sequence data from new technologies we need an improved understanding of the parametric complexity of the assembly problem. In this work, we provide a first theoretical study in this direction, exploring the connections between repeat complexity, read lengths, overlap lengths and coverage in determining the “hard ” instances of the assembly problem. Our work suggests at least two ways in which existing assemblers can be extended in a rigorous fashion, in addition to delineating directions for future theoretical investigations. 1

Motivation: High throughput sequencing technologies produce large sets of short reads that may contain errors. These sequencing errors make de novo assembly challenging. Error correction aims to reduce the error rate prior assembly. Many de novo sequencing projects use reads from several sequencing technologies to get the benets of all used technologies and to alleviate their shortcomings. However, com-bining such a mixed set of reads is problematic as many tools are specic to one sequencing platform. The SOLiD sequencing platform is especially problematic in this regard because of the two base color coding of the reads. Therefore new tools for working with mixed read sets are needed. Results: We present an error correction tool for correcting substi-tutions, insertions, and deletions in a mixed set of reads produced by various sequencing platforms. We rst develop a method for cor-recting reads from any sequencing technology producing base space reads such as the SOLEXA/Illumina and Roche/454 Life Sciences sequencing platforms. We then further rene the algorithm to correct the color space reads from the Applied Biosystems SOLiD sequen-cing platform together with normal base space reads. Our new tool is based on the SHREC program that is aimed at correcting SOLEXA/Illumina reads. Our experiments show that we can detect errors with 99 % sensitivity and over 98 % specicity if the combined sequencing coverage of the sets is at least 12. We also show that the error rate of the reads is greatly reduced. Availability: The JAVA source code is freely available at

Motivation: Consensus sequence generation is impor-tant in many kinds of sequence analysis ranging from sequence assembly to profile-based iterative search meth-ods. However, how can a consensus be constructed when its inherent assumption—that the aligned sequences form a single linear consensus—is not true? Results: Partial Order Alignment (POA) enables construc-tion and analysis of multiple sequence alignments as di-rected acyclic graphs containing complex branching struc-ture. Here we present a dynamic programming algorithm (heaviest bundle) for generating multiple consensus se-quences from such complex alignments. The number and relationships of these consensus sequences reveals the degree of structural complexity of the source alignment. This is a powerful and general approach for analyzing and visualizing complex alignment structures, and can be ap-plied to any alignment. We illustrate its value for analyzing expressed sequence alignments to detect alternative splic-ing, reconstruct full length mRNA isoform sequences from EST fragments, and separate paralog mixtures that can cause incorrect SNP predictions. Availability: The heaviest bundle source code is available

While recently developed short-read sequencing technologies may dramatically reduce the sequencing cost and eventually achieve the $1000 goal for re-sequencing, their limitations prevent the de novo sequencing of eukaryotic genomes with the standard shotgun sequencing protocol. We present SHRAP (SHort Read Assembly Protocol), a sequencing protocol and assembly methodology that utilizes high-throughput short-read technologies. We describe a variation on hierarchical sequencing with two crucial differences: (1) we select a clone library from the genome randomly rather than as a tiling path and (2) we sample clones from the genome at high coverage and reads from the clones at low coverage. We assume that 200 bp read lengths with a 1 % error rate and inexpensive random fragment cloning on whole mammalian genomes is feasible. Our assembly methodology is based on first ordering the clones and subsequently performing read assembly in three stages: (1) local assemblies of regions significantly smaller than a clone size, (2) clone-sized assemblies of the results of stage 1, and (3) chromosome-sized assemblies. By aggressively localizing the assembly problem during the first stage, our method succeeds in assembling short, unpaired reads sampled from repetitive genomes. We tested our assembler using simulated reads from D. melanogaster and human chromosomes 1, 11, and 21, and produced assemblies with large sets of contiguous sequence and a misassembly rate comparable to other draft assemblies. Tested on D. melanogaster and the entire human genome, our clone-ordering method produces accurate maps, thereby localizing fragment assembly and enabling the parallelization of the subsequent steps of our pipeline. Thus, we have demonstrated that truly inexpensive de novo sequencing of mammalian

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.