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Deconvolute individual genomes from metagenome sequences through short read clustering.
Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality
Likelihood-based inference of B-cell clonal families
The human immune system depends on a highly diverse collection of
antibody-making B cells. B cell receptor sequence diversity is generated by a
random recombination process called "rearrangement" forming progenitor B cells,
then a Darwinian process of lineage diversification and selection called
"affinity maturation." The resulting receptors can be sequenced in high
throughput for research and diagnostics. Such a collection of sequences
contains a mixture of various lineages, each of which may be quite numerous, or
may consist of only a single member. As a step to understanding the process and
result of this diversification, one may wish to reconstruct lineage membership,
i.e. to cluster sampled sequences according to which came from the same
rearrangement events. We call this clustering problem "clonal family
inference." In this paper we describe and validate a likelihood-based framework
for clonal family inference based on a multi-hidden Markov Model (multi-HMM)
framework for B cell receptor sequences. We describe an agglomerative algorithm
to find a maximum likelihood clustering, two approximate algorithms with
various trade-offs of speed versus accuracy, and a third, fast algorithm for
finding specific lineages. We show that under simulation these algorithms
greatly improve upon existing clonal family inference methods, and that they
also give significantly different clusters than previous methods when applied
to two real data sets
Recovering complete and draft population genomes from metagenome datasets.
Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
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