scholarly journals CellProfiler Analyst: interactive data exploration, analysis, and classification of large biological image sets

2016 ◽  
Author(s):  
D. Dao ◽  
A. N. Fraser ◽  
J. Hung ◽  
V. Ljosa ◽  
S. Singh ◽  
...  
Keyword(s):  
Supervised Machine Learning ◽  
Supplementary Information ◽  
Link Type ◽  
Learning Capabilities ◽  
Visualization Tools ◽  
Interactive Data ◽  
Supplementary Text ◽  
Microsoft Windows ◽  
Benchmarking Performance

AbstractSummaryCellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (in Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).AvailabilityCellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org/releases and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the [email protected] informationSupplementary Text 1: Manual to CellProfiler Analyst; updated versions are available at CellProfiler.org/CPASupplementary Data 1: Benchmarking performance of classifiers in CPA 2.0 versus CPA 1.0

scholarly journals FOCUS2: agile and sensitive classification of metagenomics data using a reduced database

2016 ◽  
Author(s):  
Genivaldo Gueiros Z. Silva ◽  
Bas E. Dutilh ◽  
Robert A. Edwards
Keyword(s):  
Microbial Community ◽  
Dna Sequences ◽  
Computational Method ◽  
Environmental Research ◽  
Supplementary Information ◽  
Sequence Classification ◽  
Computationally Efficient ◽  
Link Type ◽  
Metagenomics Data

ABSTRACTSummaryMetagenomics approaches rely on identifying the presence of organisms in the microbial community from a set of unknown DNA sequences. Sequence classification has valuable applications in multiple important areas of medical and environmental research. Here we introduce FOCUS2, an update of the previously published computational method FOCUS. FOCUS2 was tested with 10 simulated and 543 real metagenomes demonstrating that the program is more sensitive, faster, and more computationally efficient than existing methods.AvailabilityThe Python implementation is freely available at https://edwards.sdsu.edu/FOCUS2.Supplementary informationavailable at Bioinformatics online.

scholarly journals Phoenix Enhancer: proteomics data mining using clustered spectra

2019 ◽  
Author(s):  
Mingze Bai ◽  
Chunyuan Qin ◽  
Kunxian Shu ◽  
Johannes Griss ◽  
Yasset Perez-Riverol ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Proteomics Data ◽  
Web Based ◽  
Tandem Mass Spectra ◽  
Link Type ◽  
False Discovery ◽  
Cluster Data ◽  
Public Data ◽  
Visualization Tools ◽  
Individual Dataset

AbstractMotivationSpectrum clustering has been used to enhance proteomics data analysis: some originally unidentified spectra can potentially be identified and individual peptides can be evaluated to find potential mis-identifications by using clusters of identified spectra. The Phoenix Enhancer provides an infrastructure to analyze tandem mass spectra and the corresponding peptides in the context of previously identified public data. Based on PRIDE Cluster data and a newly developed pipeline, four functionalities are provided: i) evaluate the original peptide identifications in an individual dataset, to find low confidence peptide spectrum matches (PSMs) which could correspond to mis-identifications; ii) provide confidence scores for all originally identified PSMs, to help users evaluate their quality (complementary to getting a global false discovery rate); iii) identify potential new PSMs for originally unidentified spectra; and iv) provide a collection of browsing and visualization tools to analyze and export the results. In addition to the web based service, the code is open-source and easy to re-deploy on local computers using Docker containers.AvailabilityThe service of Phoenix Enhancer is available at http://enhancer.ncpsb.org. All source code is freely available in GitHub (https://github.com/phoenix-cluster/) and can be deployed in the Cloud and HPC [email protected] informationSupplementary data are available online.

scholarly journals deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

2016 ◽  
Author(s):  
Dengfeng Guan ◽  
Bo Liu ◽  
Yadong Wang
Keyword(s):  
Source Code ◽  
Classification Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Supplementary Data ◽  
Link Type ◽  
Memory Footprint ◽  
Supplementary Material ◽  
De Bruijn

AbstractSummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.Availabilitythe C++ source code of deSPI is available at https://github.com/hitbc/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets

2016 ◽  
Vol 32 (20) ◽  
pp. 3210-3212 ◽  
Author(s):  
David Dao ◽  
Adam N. Fraser ◽  
Jane Hung ◽  
Vebjorn Ljosa ◽  
Shantanu Singh ◽  
...  
Keyword(s):  
Data Exploration ◽  
Interactive Data ◽  
Biological Image

Nocardia bovistercoris sp. nov., an actinobacterium isolated from cow dung

2019 ◽  
Vol 71 (3) ◽  
Author(s):  
Xue Zhang ◽  
Lida Zhang ◽  
XiaoYan Yu ◽  
Jing Zhang ◽  
Yanjie Jiao ◽  
...  
Keyword(s):  
Type Species ◽  
Cow Dung ◽  
Rrna Gene ◽  
Content Type ◽  
Mycolic Acids ◽  
Link Type ◽  
Pr China ◽  
Taxonomic Characterization ◽  
Reference Strains

A novel actinobacterium, designated strain NEAU-351T, was isolated from cow dung collected from Shangzhi, Heilongjiang Province, northeast PR China and characterized using a polyphasic approach. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain NEAU-351T belonged to the genus Nocardia , with the highest similarity (98.96 %) to Nocardia takedensis DSM 44801T and less than 98.0 % identity with other type strains of the genus Nocardia . The polar lipids consisted of diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylinositol. The major menaquinone was observed to contain MK-8(H4, ω-cycl) (78.2 %). The fatty acid profile mainly consisted of C16 : 0, C18 : 1  ω9c and 10-methyl C18 : 0. Mycolic acids were present. The genomic DNA G+C content of strain NEAU-351T was 68.1 mol%. In addition, the average nucleotide identity values between strain NEAU-351T and its reference strains, Nocardia takedensis DSM 44801T and Nocardia arizonensis NBRC 108935T, were found to be 81.4 and 82.9 %, respectively, and the level of digital DNA–DNA hybridization between them were 24.8 % (22.5–27.3 %) and 26.3 % (24–28.8 %), respectively. Here we report on the taxonomic characterization and classification of the isolate and propose that strain NEAU-351T represents a new species of the genus Nocardia , for which the name Nocardia bovistercoris is proposed. The type strain is NEAU-351T (=CCTCC AA 2019090T=DSM 110681T).

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods
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