David Weese

Bio: David Weese is an academic researcher from Georgia College & State University. The author has contributed to research in topics: Population & Data structure. The author has an hindex of 20, co-authored 46 publications receiving 1920 citations. Previous affiliations of David Weese include University of Michigan & Pierre-and-Marie-Curie University.

SeqAn An efficient, generic C++ library for sequence analysis

Abstract: Background The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome [1] would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.

Phylogenomics of Lophotrochozoa with Consideration of Systematic Error.

Abstract: Phylogenomic studies have improved understanding of deep metazoan phylogeny and show promise for resolving incongruences among analyses based on limited numbers of loci. One region of the animal tree that has been especially difficult to resolve, even with phylogenomic approaches, is relationships within Lophotrochozoa (the animal clade that includes molluscs, annelids, and flatworms among others). Lack of resolution in phylogenomic analyses could be due to insufficient phylogenetic signal, limitations in taxon and/or gene sampling, or systematic error. Here, we investigated why lophotrochozoan phylogeny has been such a difficult question to answer by identifying and reducing sources of systematic error. We supplemented existing data with 32 new transcriptomes spanning the diversity of Lophotrochozoa and constructed a new set of Lophotrochozoa-specific core orthologs. Of these, 638 orthologous groups (OGs) passed strict screening for paralogy using a tree-based approach. In order to reduce possible sources of systematic error, we calculated branch-length heterogeneity, evolutionary rate, percent missing data, compositional bias, and saturation for each OG and analyzed increasingly stricter subsets of only the most stringent (best) OGs for these five variables. Principal component analysis of the values for each factor examined for each OG revealed that compositional heterogeneity and average patristic distance contributed most to the variance observed along the first principal component while branch-length heterogeneity and, to a lesser extent, saturation contributed most to the variance observed along the second. Missing data did not strongly contribute to either. Additional sensitivity analyses examined effects of removing taxa with heterogeneous branch lengths, large amounts of missing data, and compositional heterogeneity. Although our analyses do not unambiguously resolve lophotrochozoan phylogeny, we advance the field by reducing the list of viable hypotheses. Moreover, our systematic approach for dissection of phylogenomic data can be applied to explore sources of incongruence and poor support in any phylogenomic data set. [Annelida; Brachiopoda; Bryozoa; Entoprocta; Mollusca; Nemertea; Phoronida; Platyzoa; Polyzoa; Spiralia; Trochozoa.].

RazerS—fast read mapping with sensitivity control

Abstract: Second-generation sequencing technologies deliver DNA sequence data at unprecedented high throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. Due to the large amounts of data, efficient algorithms and implementations are crucial for this task. We present an efficient read mapping tool called RazerS. It allows the user to align sequencing reads of arbitrary length using either the Hamming distance or the edit distance. Our tool can work either lossless or with a user-defined loss rate at higher speeds. Given the loss rate, we present an approach that guarantees not to lose more reads than specified. This enables the user to adapt to the problem at hand and provides a seamless tradeoff between sensitivity and running time. [RazerS is freely available at http://www.seqan.de/projects/razers.html.]

RazerS 3

Abstract: Motivation: During the past years, next-generation sequencing has become a key technology for many applications in the biomedical sciences. Throughput continues to increase and new protocols provide longer reads than currently available. In almost all applications, read mapping is a first step. Hence, it is crucial to have algorithms and implementations that perform fast, with high sensitivity, and are able to deal with long reads and a large absolute number of insertions and deletions. Results: RazerS is a read mapping program with adjustable sensitivity based on counting q-grams. In this work, we propose the successor RazerS 3, which now supports shared-memory parallelism, an additional seed-based filter with adjustable sensitivity, a much faster, banded version of the Myers’ bit-vector algorithm for verification, memory-saving measures and support for the SAM output format. This leads to a much improved performance for mapping reads, in particular, long reads with many errors. We extensively compare RazerS 3 with other popular read mappers and show that its results are often superior to them in terms of sensitivity while exhibiting practical and often competitive run times. In addition, RazerS 3 works without a pre-computed index. Availability and Implementation: Source code and binaries are freely available for download at http://www.seqan.de/projects/razers. RazerS 3 is implemented in C++ and OpenMP under a GPL license using the SeqAn library and supports Linux, Mac OS X and Windows. Contact: david.weese@fu-berlin.de Supplementary information:Supplementary data are available at Bioinformatics online.

Alignment of Next-Generation Sequencing Reads

Abstract: High-throughput DNA sequencing has considerably changed the possibilities for conducting biomedical research by measuring billions of short DNA or RNA fragments. A central computational problem, and for many applications a first step, consists of determining where the fragments came from in the original genome. In this article, we review the main techniques for generating the fragments, the main applications, and the main algorithmic ideas for computing a solution to the read alignment problem. In addition, we describe pitfalls and difficulties connected to determining the correct positions of reads.

Fast and accurate short read alignment with Burrows–Wheeler transform

Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

TopHat: discovering splice junctions with RNA-Seq

Abstract: Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: ude.dmu.sc@eloc Supplementary information: Supplementary data are available at Bioinformatics online.