23 research outputs found
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses
International Coordination of Long-Term Ocean Biology Time Series Derived from Satellite Ocean Color Data
[ABSTRACT] In this paper, we will describe plans to coordinate the initial development of long-term ocean biology time series derived from global ocean color observations acquired by the United States, Japan and Europe, Specifically, we have been commissioned by the International Ocean Color Coordinating Group (IOCCG) to coordinate the development of merged products derived from the OCTS, SeaWiFS, MODIS, MERIS and GLI imagers. Each of these missions will have been launched by the year 2002 and will have produced global ocean color data products. Our goal is to develop and document the procedures to be used by each space agency (NASA, NASDA, and ESA) to merge chlorophyll, primary productivity, and other products from these missions. This coordination is required to initiate the production of long-term ocean biology time series which will be continued operationally beyond 2002. The purpose of the time series is to monitor interannual to decadal-scale variability in oceanic primary productivity and to study the effects of environmental change on upper ocean biogeochemical processes
Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis
Metaproteomics provides a direct
measure of the functional information
by investigating all proteins expressed by a microbiota. However,
due to the complexity and heterogeneity of microbial communities,
it is very hard to construct a sequence database suitable for a metaproteomic
study. Using a public database, researchers might not be able to identify
proteins from poorly characterized microbial species, while a sequencing-based
metagenomic database may not provide adequate coverage for all potentially
expressed protein sequences. To address this challenge, we propose
a metagenomic taxonomy-guided database-search strategy (MT), in which
a merged database is employed, consisting of both taxonomy-guided
reference protein sequences from public databases and proteins from
metagenome assembly. By applying our MT strategy to a mock microbial
mixture, about two times as many peptides were detected as with the
metagenomic database only. According to the evaluation of the reliability
of taxonomic attribution, the rate of misassignments was comparable
to that obtained using an a priori matched database. We also evaluated
the MT strategy with a human gut microbial sample, and we found 1.7
times as many peptides as using a standard metagenomic database. In
conclusion, our MT strategy allows the construction of databases able
to provide high sensitivity and precision in peptide identification
in metaproteomic studies, enabling the detection of proteins from
poorly characterized species within the microbiota
Additional file 3: of A first immunohistochemistry study of transketolase and transketolase-like 1 expression in canine hyperplastic and neoplastic mammary lesions
Graphical representation (box-plot) of TKTL1 immunohistochemical evaluation. Immunoreactivity scores (IRS) of normal mammary glands (n = 6), ductal hyperplasias (n = 3), benign tumors (n = 11) and carcinomas (n = 17), with statistical differences between lesions. Different letters (a, b, c, d) indicate significant differences (P < 0.05), red line (median values), Kruskal-Wallis ANOVA followed by Dunn’s post hoc test. (TIF 970 kb
Additional file 2: Figure S1. of Potential and active functions in the gut microbiota of a healthy human cohort
Principal component analysis plots related to taxonomic and functional features. MG data are in blue, while MP data are in red. Each dot (with different shape) represents a different human subject. (A) phyla; (B) genera; (C) KOGs; (D) KOG-phylum combinations. (PNG 2001 kb
Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses
Unipept Desktop 2.0 is the most recent iteration of the
Unipept
Desktop tool that adds support for the analysis of metaproteogenomics
datasets. Unipept Desktop now supports the automatic construction
of targeted protein reference databases that only contain proteins
(originating from the UniProtKB resource) associated with a predetermined
list of taxa. This improves both the taxonomic and functional resolution
of a metaproteomic analysis and yields several technical advantages.
By limiting the proteins present in a reference database, it is also
possible to perform (meta)proteogenomics analyses. Since the protein
reference database resides on the user’s local machine, they
have complete control over the database used during an analysis. Data
no longer need to be transmitted over the Internet, decreasing the
time required for an analysis and better safeguarding privacy-sensitive
data. As a proof of concept, we present a case study in which a human
gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using
different targeted databases based on matched 16S rRNA gene sequencing
data
Additional file 5: Dataset S2. of Potential and active functions in the gut microbiota of a healthy human cohort
Relative abundance and differential analysis outputs concerning Firmicutes and Bacteroidetes KOGs, according to MG and MP data. (XLSX 101 kb
Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses
Unipept Desktop 2.0 is the most recent iteration of the
Unipept
Desktop tool that adds support for the analysis of metaproteogenomics
datasets. Unipept Desktop now supports the automatic construction
of targeted protein reference databases that only contain proteins
(originating from the UniProtKB resource) associated with a predetermined
list of taxa. This improves both the taxonomic and functional resolution
of a metaproteomic analysis and yields several technical advantages.
By limiting the proteins present in a reference database, it is also
possible to perform (meta)proteogenomics analyses. Since the protein
reference database resides on the user’s local machine, they
have complete control over the database used during an analysis. Data
no longer need to be transmitted over the Internet, decreasing the
time required for an analysis and better safeguarding privacy-sensitive
data. As a proof of concept, we present a case study in which a human
gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using
different targeted databases based on matched 16S rRNA gene sequencing
data
Comparison of metaproteomic data obtained with different databases.
<p>A) Number of peptide sequences (left) and peptide-spectrum matches (PSMs, right) identified in the 9MM using different sequence databases (FDR<1%). B) Left, Venn diagram illustrating the peptide distribution among four different DB classes. Center, Venn diagram illustrating the peptide distribution among all NCBI-, TrEMBL- and SwissProt-based DBs used in this study. Right, Venn diagram illustrating the peptide distribution among all DBs with generic microbial taxonomy (BFV), genus-specific taxonomy (G), and species-specific taxonomy (S).</p