Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Background: The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results: In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions: Many current genome assemblers produced useful assemblies, containing a significant representation of their genes and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Examining the antecedents and consequences of mobile travel app engagement.

How and why customers engage with mobile travel apps is vital to mobile marketing of travel-related companies. This paper discusses the antecedents and consequences of mobile travel app engagement. Specifically, this study aims to understand how travel app attributes stimulate mobile travel app engagement and lead to purchase intention. A research model is established based on the Stimulus-Organism-Response (S-O-R) model and the model is tested by Partial Least Squares Path Modeling (PLS-PM). The results show that ease of use, compatibility, and UI attractiveness positively influence mobile travel app engagement, and in turn, affect purchase intention. Furthermore, a multi-group analysis shows that the attributes affecting mobile travel app engagement differ across different customer groups. This paper discusses some theoretical and practical implications

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease

Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges. One of them is the need for high-quality training samples. This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space. We migrated training samples collected for Washington, D. C. in 1979 to classify the city’s land covers in 1982 and 1984. The classifier trained with Washington, D. C.’s samples were used in classifying Boston’s and Tokyo’s land covers. The results showed that the overall accuracies achieved using migrated samples in 1982 (66.67%) and 1984 (65.67%) for Washington, D. C. were comparable to that of 1979 (68.67%) using a random forest classifier. Migration of training samples between cities in the same urban ecoregion, i.e. Washington, D. C., and Boston, achieved higher overall accuracy (59.33%) than cities in the different ecoregions (Tokyo, 50.33%). We concluded that migrating training samples across time and space in the same urban ecoregion are feasible. Our findings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale

Design and Optimization of an S-Band MEMS Bandpass Filter Based on Aggressive Space Mapping

Aggressive space mapping (ASM) is a common filter simulation and debugging method. It plays an important role in the field of microwave device design. This paper introduces ASM and presents the design and fabrication of a compact fifth-order microstrip interdigital filter with a center frequency of 2.5 GHz and a relative bandwidth of 10% using ASM. The filter used a double-layer silicon substrate structure and stepped impedance resonators (SIRs) and was optimized by ASM. After five iterations, the filter achieved the design specification, which greatly improves the efficiency of the filter design compared with the traditional method. It was fabricated on high-resistance silicon wafers by micro-electro-mechanical systems (MEMSs) technology, and the final size of the chip is 9.5 mm × 7.6 mm × 0.8 mm. The measurement results show that the characteristics of the filter are similar to the simulation results, which also shows the efficiency and precision of the ASM algorithm