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The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined 'IMF' classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F). ispartof: NATURE GENETICS vol:54 issue:7 pages:963-+ ispartof: location:United States status: published

Assessment of human movement performance in activities of daily living (ADL) is a key component in clinical and rehabilitation settings. Motion capture technology is an effective method for objective assessment of human movement. Existing databases capture human movement and ADL performance primarily in the Western population, and there are no Asian databases to date. This is despite the fact that Asian anthropometrics influence movement kinematics and kinetics. This paper details the protocol in the first phase of the largest Asian normative human movement database. Data collection has commenced, and this paper reports 10 healthy participants. Twelve tasks were performed and data was collected using Qualisys motion capture system, force plates and instrumented table and chair. In phase two, human movement of individuals with stroke and knee osteoarthritis will be captured. This can have great potential for benchmarking with the normative human movement captured in phase one and predicting recovery and progression of movement for patients. With individualised progression, it will offer the development of personalised therapy protocols in rehabilitation., Measurement(s) voluntary movement behavior • grip strength measurement • Pinch Strength Technology Type(s) motion capture system • force plates • sensor • dynamometry Factor Type(s) gait • Timed Get Up and Go Test • balance • reaching • key turning • hand to mouth • hand to back • hand on head • cross obstacle • folding towel • step up and down Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Asia Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12808187

Background While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocal-mode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. In addition, the incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis. Results In this work, both the speed of HMMER3 and glocal-mode alignment of HMMER2 are combined within the xHMMER3x2 framework for tackling the large-scale domain annotation task. Briefly, HMMER3 is utilized for initial domain detection so that HMMER2 can subsequently perform the glocal-mode, sequence-to-full-domain alignments for the detected HMMER3 hits. An E-value calibration procedure is required to ensure that the search space by HMMER2 is sufficiently replicated by HMMER3. We find that the latter is straightforwardly possible for ~80% of the models in the Pfam domain library (release 29). However in the case of the remaining ~20% of HMMER3 domain models, the respective HMMER2 counterparts are more sensitive. Thus, HMMER3 searches alone are insufficient to ensure sensitivity and a HMMER2-based search needs to be initiated. When tested on the set of UniProt human sequences, xHMMER3x2 can be configured to be between 7× and 201× faster than HMMER2, but with descending domain detection sensitivity from 99.8 to 95.7% with respect to HMMER2 alone; HMMER3’s sensitivity was 95.7%. At extremes, xHMMER3x2 is either the slow glocal-mode HMMER2 or the fast HMMER3 with glocal-mode. Finally, the E-values to false-positive rates (FPR) mapping by xHMMER3x2 allows E-values of different model builds to be compared, so that any annotation discrepancies in a large-scale annotation exercise can be flagged for further examination by dissectHMMER. Conclusion The xHMMER3x2 workflow allows large-scale domain annotation speed to be drastically improved over HMMER2 without compromising for domain-detection with regard to sensitivity and sequence-to-domain alignment incompleteness. The xHMMER3x2 code and its webserver (for Pfam release 27, 28 and 29) are freely available at http://xhmmer3x2.bii.a-star.edu.sg/. Reviewers Reviewed by Thomas Dandekar, L. Aravind, Oliviero Carugo and Shamil Sunyaev. For the full reviews, please go to the Reviewers’ comments section. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0163-0) contains supplementary material, which is available to authorized users.

Background Annotation transfer for function and structure within the sequence homology concept essentially requires protein sequence similarity for the secondary structural blocks forming the fold of a protein. A simplistic similarity approach in the case of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc.) is not justified and a pertinent source for mistaken homologies. The latter is either due to positional sequence conservation as a result of a very simple, physically induced pattern or integral sequence properties that are critical for function. Furthermore, against the backdrop that the number of well-studied proteins continues to grow at a slow rate, it necessitates for a search methodology to dive deeper into the sequence similarity space to connect the unknown sequences to the well-studied ones, albeit more distant, for biological function postulations. Results Based on our previous work of dissecting the hidden markov model (HMMER) based similarity score into fold-critical and the non-globular contributions to improve homology inference, we propose a framework-dissectHMMER, that identifies more fold-related domain hits from standard HMMER searches. Subsequent statistical stratification of the fold-related hits into cohorts of functionally-related domains allows for the function postulation of the query sequence. Briefly, the technical problems as to how to recognize non-globular parts in the domain model, resolve contradictory HMMER2/HMMER3 results and evaluate fold-related domain hits for homology, are addressed in this work. The framework is benchmarked against a set of SCOP-to-Pfam domain models. Despite being a sequence-to-profile method, dissectHMMER performs favorably against a profile-to-profile based method-HHsuite/HHsearch. Examples of function annotation using dissectHMMER, including the function discovery of an uncharacterized membrane protein Q9K8K1_BACHD (WP_010899149.1) as a lactose/H+ symporter, are presented. Finally, dissectHMMER webserver is made publicly available at http://dissecthmmer.bii.a-star.edu.sg. Conclusions The proposed framework-dissectHMMER, is faithful to the original inception of the sequence homology concept while improving upon the existing HMMER search tool through the rescue of statistically evaluated false-negative yet fold-related domain hits to the query sequence. Overall, this translates into an opportunity for any novel protein sequence to be functionally characterized. Reviewers This article was reviewed by Masanori Arita, Shamil Sunyaev and L. Aravind. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0068-3) contains supplementary material, which is available to authorized users.

The innate immune system is fundamental to the recognition of pathogens, triggering of immune-inflammatory response and host defense. Recent advance in this area has resulted in enormous amount of data, which are stored across different databases. Integrating relevant information from these different data sources is difficult because of their heterogeneous nature and dispersed physical location. We present here a single portal system, Cell Interaction Knowledgebase, with focus on the innate immunity. In particular, the knowledgebase houses comprehensive information on innate immune cells and cytokines/chemokines which are the principal mediators of communication among the immune cells. Currently the knowledgebase consists of extensive information on 2 major innate immune cell types (Macrophages and Dendritic cells) and 7 6 cytokines/chemokines for both human and mouse. In addition, intra-cellular molecular interactions and inter-cellular interactions involved in the innate immunity are curated and presented in an interactive and dynamic manner by animated pathways and query-driven cell-interaction map respectively. This is one of the first databases that houses extensive phenotypic, signaling, genomic, proteomic and knockout data on both the innate immune cells and their attendant cytokines/chemokines, and is aimed to evolve as a one-stop-shop for immunologists. The first version of database is available at http://cell-interaction.bii.a-star.edu.sg/.

Detection of cell nucleus is critical in microscopy image analysis and ellipse detection plays an important role because most nuclei are elliptical in shapes. We developed an ellipse detection algorithm based on the Mumford-Shah model that inherits its superior properties. In our ellipse detector, the active contours in the Mumford-Shah model are constrained to be non-overlapping ellipses. A quantitative comparison with the randomized Hough transform shows that the Mumford-Shah based approach detects nucleus significantly better on our data sets.