Drastic population fluctuations explain the rapid extinction of the passenger pigeon.

To assess the role of human disturbances in species' extinction requires an understanding of the species population history before human impact. The passenger pigeon was once the most abundant bird in the world, with a population size estimated at 3-5 billion in the 1800s; its abrupt extinction in 1914 raises the question of how such an abundant bird could have been driven to extinction in mere decades. Although human exploitation is often blamed, the role of natural population dynamics in the passenger pigeon's extinction remains unexplored. Applying high-throughput sequencing technologies to obtain sequences from most of the genome, we calculated that the passenger pigeon's effective population size throughout the last million years was persistently about 1/10,000 of the 1800's estimated number of individuals, a ratio 1,000-times lower than typically found. This result suggests that the passenger pigeon was not always super abundant but experienced dramatic population fluctuations, resembling those of an "outbreak" species. Ecological niche models supported inference of drastic changes in the extent of its breeding range over the last glacial-interglacial cycle. An estimate of acorn-based carrying capacity during the past 21,000 y showed great year-to-year variations. Based on our results, we hypothesize that ecological conditions that dramatically reduced population size under natural conditions could have interacted with human exploitation in causing the passenger pigeon's rapid demise. Our study illustrates that even species as abundant as the passenger pigeon can be vulnerable to human threats if they are subject to dramatic population fluctuations, and provides a new perspective on the greatest human-caused extinction in recorded history.

Assembler for de novo assembly of large genomes.

Assembling a large genome using next generation sequencing reads requires large computer memory and a long execution time. To reduce these requirements, we propose an extension-based assembler, called JR-Assembler, where J and R stand for "jumping" extension and read "remapping." First, it uses the read count to select good quality reads as seeds. Second, it extends each seed by a whole-read extension process, which expedites the extension process and can jump over short repeats. Third, it uses a dynamic back trimming process to avoid extension termination due to sequencing errors. Fourth, it remaps reads to each assembled sequence, and if an assembly error occurs by the presence of a repeat, it breaks the contig at the repeat boundaries. Fifth, it applies a less stringent extension criterion to connect low-coverage regions. Finally, it merges contigs by unused reads. An extensive comparison of JR-Assembler with current assemblers using datasets from small, medium, and large genomes shows that JR-Assembler achieves a better or comparable overall assembly quality and requires lower memory use and less central processing unit time, especially for large genomes. Finally, a simulation study shows that JR-Assembler achieves a superior performance on memory use and central processing unit time than most current assemblers when the read length is 150 bp or longer, indicating that the advantages of JR-Assembler over current assemblers will increase as the read length increases with advances in next generation sequencing technology.

Functional characterization of cellulases identified from the cow rumen fungus Neocallimastix patriciarum W5 by transcriptomic and secretomic analyses.

BACKGROUND: Neocallimastix patriciarum is one of the common anaerobic fungi in the digestive tracts of ruminants that can actively digest cellulosic materials, and its cellulases have great potential for hydrolyzing cellulosic feedstocks. Due to the difficulty in culture and lack of a genome database, it is not easy to gain a global understanding of the glycosyl hydrolases (GHs) produced by this anaerobic fungus. RESULTS: We have developed an efficient platform that uses a combination of transcriptomic and proteomic approaches to N. patriciarum to accelerate gene identification, enzyme classification and application in rice straw degradation. By conducting complementary studies of transcriptome (Roche 454 GS and Illumina GA IIx) and secretome (ESI-Trap LC-MS/MS), we identified 219 putative GH contigs and classified them into 25 GH families. The secretome analysis identified four major enzymes involved in rice straw degradation: ß-glucosidase, endo-1,4-ß-xylanase, xylanase B and Cel48A exoglucanase. From the sequences of assembled contigs, we cloned 19 putative cellulase genes, including the GH1, GH3, GH5, GH6, GH9, GH18, GH43 and GH48 gene families, which were highly expressed in N. patriciarum cultures grown on different feedstocks. CONCLUSIONS: These GH genes were expressed in Pichia pastoris and/or Saccharomyces cerevisiae for functional characterization. At least five novel cellulases displayed cellulytic activity for glucose production. One ß-glucosidase (W5-16143) and one exocellulase (W5-CAT26) showed strong activities and could potentially be developed into commercial enzymes.

GR-Aligner: an algorithm for aligning pairwise genomic sequences containing rearrangement events.

MOTIVATION: Homologous genomic sequences between species usually contain different rearrangement events. Whether some specific patterns existed in the breakpoint regions that caused such events to occur is still unclear. To resolve this question, it is necessary to determine the location of breakpoints at the nucleotide level. The availability of sequences near breakpoints would further facilitate the related studies. We thus need a tool that can identify breakpoints and align the neighboring sequences. Although local alignment tools can detect rearrangement events, they only report a set of discontinuous alignments, where the detailed alignments in the breakpoint regions are usually missing. Global alignment tools are even less appropriate for these tasks since most of them are designed to align the conserved regions between sequences in a consistent order, i.e. they do not consider rearrangement events. RESULTS: We propose an effective and efficient pairwise sequence alignment algorithm, called GR-Aligner (Genomic Rearrangement Aligner), which can find breakpoints of rearrangement events by integrating the forward and reverse alignments of the breakpoint regions flanked by homologously rearranged sequences. In addition, GR-Aligner also provides an option to view the alignments of sequences extended to the breakpoints. These outputs provide materials for studying possible evolutionary mechanisms and biological functionalities of the rearrangement.