A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio

Motivation: With the rapid development of next-generation sequencing techniques, metagenomics, also known as environmental genomics, has emerged as an exciting research area that enables us to analyze the microbial environment in which we live. An important step for metagenomic data analysis is the identification and taxonomic characterization of DNA fragments (reads or contigs) resulting from sequencing a sample of mixed species. This step is referred to as 'binning'. Binning algorithms that are based on sequence similarity and sequence composition markers rely heavily on the reference genomes of known microorganisms or phylogenetic markers. Due to the limited availability of reference genomes and the bias and low availability of markers, these algorithms may not be applicable in all cases. Unsupervised binning algorithms which can handle fragments from unknown species provide an alternative approach. However, existing unsupervised binning algorithms only work on datasets either with balanced species abundance ratios or rather different abundance ratios, but not both. Results: In this article, we present MetaCluster 3.0, an integrated binning method based on the unsupervised top-down separation and bottom-up merging strategy, which can bin metagenomic fragments of species with very balanced abundance ratios (say 1:1) to very different abundance ratios (e.g. 1:24) with consistently higher accuracy than existing methods. © The Author 2011. Published by Oxford University Press. All rights reserved.postprin

Assessing the impact of closely-spaced intersections on traffic operations and pollutant emissions on a corridor level

Traffic lights or roundabouts along corridors are usually installed to address location-specific operational needs. An understanding of the impacts on traffic regarding to highly-congested closely-spaced intersections has not been fully addressed. Accordingly, consideration should be given to how these specific segments affect corridor performance as a whole. One mixed roundabout/traffic light/stop-controlled junctions corridor was evaluated with the microscopic traffic model (VISSIM) and emissions methodology (Vehicle Specific Power – VSP). The analysis was focused on two major intersections of the corridor, a roundabout and a traffic light spaced lower than 170 m apart under different traffic demand levels. The traffic data and corridor geometry were coded into VISSIM and compared with an alternative scenario where the traffic light was replaced by a single-lane roundabout. This research also tested a method to improve corridor performance and emissions by examining the integrated effect of the spacing between these intersections on traffic delay and vehicular emissions (carbon dioxide, monoxide carbon, nitrogen oxides, and hydrocarbons). The Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used to find the optimal spacing for these intersections. The analysis showed that the roundabout could achieve lower queue length (∼64%) and emissions (16–27%, depending on the pollutant) than the traffic light. The results also suggested that 200 m of spacing using the best traffic control would provide a moderate advantage in traffic operations and emissions as compared with the existing spacing

MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs

Abridged Track 2 AbstractsLNCS v. 9683 entitled: Bioinformatics Research and Applications: 12th International Symposium, ISBRA 2016, Minsk, Belarus, June 5-8, 2016, Proceedingslink_to_OA_fulltex

Influence of physical fitness on cognitive and academic performance in adolescents: A systematic review from 2005-2015

Background: The aim of this systematic review was to investigate the association of different components of physical fitness on cognitive performance (CP) and academic performance (AP) in adolescents, taking into account potential confounders. Method: Studies were identified in four databases (Pubmed, SportDiscus, Web of Science, and ProQuest) from January 2005 through to January 2015. A total of 21 articles met the inclusion criteria. Results: 8 studies showed association between physical fitness and CP, and 11 studies with AP. Cardiorespiratory fitness, speed-agility, motor coordination, and perceptual-motor skill are the highest measures associated with CP and AP. However, the findings on strength and flexibility are unclear. Finally, 62% of the 21 studies used confounders. The most controlled confounder were socioeconomic status, fatness, pubertal status, sex, and age. Conclusion: Fitness is associated with higher CP and AP. More research is needed in order to understand the causes of the differential effect of physical fitness components on CP and AP

MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices

The study of metagenomics has been much benefited from low-cost and high-throughput sequencing technologies, yet the tremendous amount of data generated make analysis like de novo assembly to consume too much computational resources. In late 2014 we released MEGAHIT v0.1 (together with a brief note of Li et al. (2015) [1]), which is the first NGS metagenome assembler that can assemble genome sequences from metagenomic datasets of hundreds of Giga base-pairs (bp) in a time- and memory-efficient manner on a single server. The core of MEGAHIT is an efficient parallel algorithm for constructing succinct de Bruijn Graphs (SdBG), implemented on a graphical processing unit (GPU). The software has been well received by the assembly community, and there is interest in how to adapt the algorithms to integrate popular assembly practices so as to improve the assembly quality, as well as how to speed up the software using better CPU-based algorithms (instead of GPU).In this paper we first describe the details of the core algorithms in MEGAHIT v0.1, and then we show the new modules to upgrade MEGAHIT to version v1.0, which gives better assembly quality, runs faster and uses less memory. For the Iowa Prairie Soil dataset (252 Gbp after quality trimming), the assembly quality of MEGAHIT v1.0, when compared with v0.1, has a significant improvement, namely, 36% increase in assembly size and 23% in N50. More interestingly, MEGAHIT v1.0 is no slower than before (even running with the extra modules). This is primarily due to a new CPU-based algorithm for SdBG construction that is faster and requires less memory. Using CPU only, MEGAHIT v1.0 can assemble the Iowa Prairie Soil sample in about 43 h, reducing the running time of v0.1 by at least 25% and memory usage by up to 50%. MEGAHIT v1.0, exhibiting a smaller memory footprint, can process even larger datasets. The Kansas Prairie Soil sample (484 Gbp), the largest publicly availa ble dataset, can now be assembled using no more than 500 GB of memory in 7.5 days. The assemblies of these datasets (and other large metgenomic datasets), as well as the software, are available at the website https://hku-bal.github.io/megabox

AC-DIAMOND: accelerating protein alignment via better SIMD parallelization and space-efficient indexing

Lecture Notes in Computer Science v. 9656 entitled: Bioinformatics and Biomedical Engineering: 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016, ProceedingsTo speed up the alignment of DNA reads or assembled contigs against a protein database has been a challenge up to now. The recent tool DIAMOND has significantly improved the speed of BLASTX and RAPSearch, while giving similar degree of sensitivity. Yet for applications like metagenomics, where large amount of data is involved, DIAMOND still takes a lot of time. This paper introduces an even faster protein alignment tool, called AC-DIAMOND, which attempts to speed up DIAMOND via better SIMD parallelization and more space-efficient indexing of the reference database; the latter allows more queries to be loaded into the memory and processed together. Experimental results show that AC-DIAMOND is about 4 times faster than DIAMOND on aligning DNA reads or contigs, while retaining the same sensitivity as DIAMOND.For example, the latest assembly of the Iowa praire soil metagenomic dataset generates over 9 milllion of contigs, with a total size about 7 Gbp; when aligning these contigs to the protein database NCBI-nr, DIAMOND takes 4 to 5 days, and AC-DIAMOND takes about 1 day. AC-DIAMOND is available for testing at http://​ac-diamond.​sourceforge.​net.Link_to_subscribed_fulltex

Mixed arteriovenous malformation and capillary telangiectasia: a rare subset of mixed vascular malformations

✓ In this report, the authors discuss the case of a patient with a mixed cerebrovascular malformation in which an arteriovenous malformation (AVM) was associated with a capillary telangiectasia. Recent reports have contained reviews of various subsets of mixed malformations. To the authors' knowledge, however, this is the first report of a mixed vascular malformation with both arterial and capillary components. The patient underwent complete resection of the AVM after presenting with a clinical hemorrhage. She required a second operation to resect the capillary telangiectasia after new symptoms developed several months following the first procedure. The authors conclude that a mixed AVM—capillary telangiectasia is a rare but distinct entity.</jats:p