BFAST: An Alignment Tool for Large Scale Genome Resequencing

BACKGROUND:The new generation of massively parallel DNA sequencers, combined with the challenge of whole human genome resequencing, result in the need for rapid and accurate alignment of billions of short DNA sequence reads to a large reference genome. Speed is obviously of great importance, but equally important is maintaining alignment accuracy of short reads, in the 25-100 base range, in the presence of errors and true biological variation. METHODOLOGY:We introduce a new algorithm specifically optimized for this task, as well as a freely available implementation, BFAST, which can align data produced by any of current sequencing platforms, allows for user-customizable levels of speed and accuracy, supports paired end data, and provides for efficient parallel and multi-threaded computation on a computer cluster. The new method is based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. The final local alignment uses a Smith-Waterman method, with gaps to support the detection of small indels. CONCLUSIONS:We compare BFAST to a selection of large-scale alignment tools -- BLAT, MAQ, SHRiMP, and SOAP -- in terms of both speed and accuracy, using simulated and real-world datasets. We show BFAST can achieve substantially greater sensitivity of alignment in the context of errors and true variants, especially insertions and deletions, and minimize false mappings, while maintaining adequate speed compared to other current methods. We show BFAST can align the amount of data needed to fully resequence a human genome, one billion reads, with high sensitivity and accuracy, on a modest computer cluster in less than 24 hours. BFAST is available at (http://bfast.sourceforge.net)

Meraculous: De Novo Genome Assembly with Short Paired-End Reads

We describe a new algorithm, meraculous, for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast Pichia stipitis. More than 95% of the genome is recovered, with no errors; half the assembled sequence is in contigs longer than 101 kilobases and in scaffolds longer than 269 kilobases. Incorporating fosmid ends recovers entire chromosomes. Meraculous relies on an efficient and conservative traversal of the subgraph of the k-mer (deBruijn) graph of oligonucleotides with unique high quality extensions in the dataset, avoiding an explicit error correction step as used in other short-read assemblers. A novel memory-efficient hashing scheme is introduced. The resulting contigs are ordered and oriented using paired reads separated by ∼280 bp or ∼3.2 kbp, and many gaps between contigs can be closed using paired-end placements. Practical issues with the dataset are described, and prospects for assembling larger genomes are discussed

Integrating Sequencing Technologies in Personal Genomics: Optimal Low Cost Reconstruction of Structural Variants

The goal of human genome re-sequencing is obtaining an accurate assembly of an individual's genome. Recently, there has been great excitement in the development of many technologies for this (e.g. medium and short read sequencing from companies such as 454 and SOLiD, and high-density oligo-arrays from Affymetrix and NimbelGen), with even more expected to appear. The costs and sensitivities of these technologies differ considerably from each other. As an important goal of personal genomics is to reduce the cost of re-sequencing to an affordable point, it is worthwhile to consider optimally integrating technologies. Here, we build a simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs). SV reconstruction is considered the most challenging step in human genome re-sequencing. (It is sometimes even harder than de novo assembly of small genomes because of the duplications and repetitive sequences in the human genome.) To this end, we formulate canonical problems that are representative of issues in reconstruction and are of small enough scale to be computationally tractable and simulatable. Using semi-realistic simulations, we show how we can combine different technologies to optimally solve the assembly at low cost. With mapability maps, our simulations efficiently handle the inhomogeneous repeat-containing structure of the human genome and the computational complexity of practical assembly algorithms. They quantitatively show how combining different read lengths is more cost-effective than using one length, how an optimal mixed sequencing strategy for reconstructing large novel SVs usually also gives accurate detection of SNPs/indels, how paired-end reads can improve reconstruction efficiency, and how adding in arrays is more efficient than just sequencing for disentangling some complex SVs. Our strategy should facilitate the sequencing of human genomes at maximum accuracy and low cost

ANDES: Statistical tools for the ANalyses of DEep Sequencing

<p>Abstract</p> <p>Background</p> <p>The advancements in DNA sequencing technologies have allowed researchers to progress from the analyses of a single organism towards the deep sequencing of a sample of organisms. With sufficient sequencing depth, it is now possible to detect subtle variations between members of the same species, or between mixed species with shared biomarkers, such as the 16S rRNA gene. However, traditional sequencing analyses of samples from largely homogeneous populations are often still based on multiple sequence alignments (MSA), where each sequence is placed along a separate row and similarities between aligned bases can be followed down each column. While this visual format is intuitive for a small set of aligned sequences, the representation quickly becomes cumbersome as sequencing depths cover loci hundreds or thousands of reads deep.</p> <p>Findings</p> <p>We have developed ANDES, a software library and a suite of applications, written in Perl and R, for the statistical ANalyses of DEep Sequencing. The fundamental data structure underlying ANDES is the position profile, which contains the nucleotide distributions for each genomic position resultant from a multiple sequence alignment (MSA). Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.</p> <p>Conclusions</p> <p>As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common. We have provided a software package and demonstrated its application on various empirically-derived datasets. Investigators may download the software from Sourceforge at <url>https://sourceforge.net/projects/andestools</url>.</p

ConDeTri - A Content Dependent Read Trimmer for Illumina Data

During the last few years, DNA and RNA sequencing have started to play an increasingly important role in biological and medical applications, especially due to the greater amount of sequencing data yielded from the new sequencing machines and the enormous decrease in sequencing costs. Particularly, Illumina/Solexa sequencing has had an increasing impact on gathering data from model and non-model organisms. However, accurate and easy to use tools for quality filtering have not yet been established. We present ConDeTri, a method for content dependent read trimming for next generation sequencing data using quality scores of each individual base. The main focus of the method is to remove sequencing errors from reads so that sequencing reads can be standardized. Another aspect of the method is to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequence data of arbitrary length and it is independent from sequencing coverage and user interaction. ConDeTri is able to trim and remove reads with low quality scores to save computational time and memory usage during de novo assemblies. Low coverage or large genome sequencing projects will especially gain from trimming reads. The method can easily be incorporated into preprocessing and analysis pipelines for Illumina data

Short clones or long clones? A simulation study on the use of paired reads in metagenomics

<p>Abstract</p> <p>Background</p> <p>Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies.</p> <p>Results</p> <p>This paper addresses the problem of taxonomical analysis of paired reads. We describe a new feature of our metagenome analysis software MEGAN that allows one to process sequencing reads in pairs and makes assignments of such reads based on the combined bit scores of their matches to reference sequences. Using this new software in a simulation study, we investigate the use of Illumina paired-sequencing in taxonomical analysis and compare the performance of single reads, short clones and long clones. In addition, we also compare against simulated Roche-454 sequencing runs.</p> <p>Conclusion</p> <p>This work shows that paired reads perform better than single reads, as expected, but also, perhaps slightly less obviously, that long clones allow more specific assignments than short ones. A new version of the program MEGAN that explicitly takes paired reads into account is available from our website.</p

A pilot study for channel catfish whole genome sequencing and de novo assembly

<p>Abstract</p> <p>Background</p> <p>Recent advances in next-generation sequencing technologies have drastically increased throughput and significantly reduced sequencing costs. However, the average read lengths in next-generation sequencing technologies are short as compared with that of traditional Sanger sequencing. The short sequence reads pose great challenges for <it>de novo </it>sequence assembly. As a pilot project for whole genome sequencing of the catfish genome, here we attempt to determine the proper sequence coverage, the proper software for assembly, and various parameters used for the assembly of a BAC physical map contig spanning approximately a million of base pairs.</p> <p>Results</p> <p>A combination of low sequence coverage of 454 and Illumina sequencing appeared to provide effective assembly as reflected by a high N50 value. Using 454 sequencing alone, a sequencing depth of 18 X was sufficient to obtain the good quality assembly, whereas a 70 X Illumina appeared to be sufficient for a good quality assembly. Additional sequencing coverage after 18 X of 454 or after 70 X of Illumina sequencing does not provide significant improvement of the assembly. Considering the cost of sequencing, a 2 X 454 sequencing, when coupled to 70 X Illumina sequencing, provided an assembly of reasonably good quality. With several software tested, Newbler with a seed length of 16 and ABySS with a K-value of 60 appear to be appropriate for the assembly of 454 reads alone and Illumina paired-end reads alone, respectively. Using both 454 and Illumina paired-end reads, a hybrid assembly strategy using Newbler for initial 454 sequence assembly, Velvet for initial Illumina sequence assembly, followed by a second step assembly using MIRA provided the best assembly of the physical map contig, resulting in 193 contigs with a N50 value of 13,123 bp.</p> <p>Conclusions</p> <p>A hybrid sequencing strategy using low sequencing depth of 454 and high sequencing depth of Illumina provided the good quality assembly with high N50 value and relatively low cost. A combination of Newbler, Velvet, and MIRA can be used to assemble the 454 sequence reads and the Illumina reads effectively. The assembled sequence can serve as a resource for comparative genome analysis. Additional long reads using the third generation sequencing platforms are needed to sequence through repetitive genome regions that should further enhance the sequence assembly.</p

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

Identification of alternative splice variants in Aspergillus flavus through comparison of multiple tandem MS search algorithms

<p>Abstract</p> <p>Background</p> <p>Database searching is the most frequently used approach for automated peptide assignment and protein inference of tandem mass spectra. The results, however, depend on the sequences in target databases and on search algorithms. Recently by using an alternative splicing database, we identified more proteins than with the annotated proteins in <it>Aspergillus flavus</it>. In this study, we aimed at finding a greater number of eligible splice variants based on newly available transcript sequences and the latest genome annotation. The improved database was then used to compare four search algorithms: Mascot, OMSSA, X! Tandem, and InsPecT.</p> <p>Results</p> <p>The updated alternative splicing database predicted 15833 putative protein variants, 61% more than the previous results. There was transcript evidence for 50% of the updated genes compared to the previous 35% coverage. Database searches were conducted using the same set of spectral data, search parameters, and protein database but with different algorithms. The false discovery rates of the peptide-spectrum matches were estimated < 2%. The numbers of the total identified proteins varied from 765 to 867 between algorithms. Whereas 42% (1651/3891) of peptide assignments were unanimous, the comparison showed that 51% (568/1114) of the RefSeq proteins and 15% (11/72) of the putative splice variants were inferred by all algorithms. 12 plausible isoforms were discovered by focusing on the consensus peptides which were detected by at least three different algorithms. The analysis found different conserved domains in two putative isoforms of UDP-galactose 4-epimerase.</p> <p>Conclusions</p> <p>We were able to detect dozens of new peptides using the improved alternative splicing database with the recently updated annotation of the <it>A. flavus </it>genome. Unlike the identifications of the peptides and the RefSeq proteins, large variations existed between the putative splice variants identified by different algorithms. 12 candidates of putative isoforms were reported based on the consensus peptide-spectrum matches. This suggests that applications of multiple search engines effectively reduced the possible false positive results and validated the protein identifications from tandem mass spectra using an alternative splicing database.</p

Read Length and Repeat Resolution: Exploring Prokaryote Genomes Using Next-Generation Sequencing Technologies

Background: There are a growing number of next-generation sequencing technologies. At present, the most cost-effective options also produce the shortest reads. However, even for prokaryotes, there is uncertainty concerning the utility of these technologies for the de novo assembly of complete genomes. This reflects an expectation that short reads will be unable to resolve small, but presumably abundant, repeats. Methodology/Principal Findings: Using a simple model of repeat assembly, we develop and test a technique that, for any read length, can estimate the occurrence of unresolvable repeats in a genome, and thus predict the number of gaps that would need to be closed to produce a complete sequence. We apply this technique to 818 prokaryote genome sequences. This provides a quantitative assessment of the relative performance of various lengths. Notably, unpaired reads of only 150nt can reconstruct approximately 50 % of the analysed genomes with fewer than 96 repeat-induced gaps. Nonetheless, there is considerable variation amongst prokaryotes. Some genomes can be assembled to near contiguity using very short reads while others require much longer reads. Conclusions: Given the diversity of prokaryote genomes, a sequencing strategy should be tailored to the organism unde