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

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

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

Improvement of the reliability of taxonomic attribution upon data filtering.

<p>Histograms showing the number of families (top), genera (middle) and species (bottom) detected upon Unipept (left) or MEGAN (right) LCA analysis using different DBs, before and after the application of a filter based on the number of taxon-specific peptides (u, unfiltered; f, filtered). The threshold was set to 0.5% of the overall number of peptides unambiguously assigned to a taxon at a particular taxonomic rank level (family, genus or species). Correct and incorrect attributions are represented in green and red, respectively. The light blue lines and numbers correspond to the number of families, genera or species actually present in the 9MM.</p

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

Reliability of taxonomic attribution using Unipept and MEGAN.

<p>Bar graphs showing taxonomic distribution of family (top), genus (middle) and species (bottom) specific peptides identified with different DBs, according to Unipept (left) or MEGAN (right) LCA analysis. Red rectangles illustrate misassignments (i.e. attributions to taxa not actually present in the 9MM), with indication of their percentage for each DB. Bacterial taxa are represented by shades of blue, whereas yeast taxa by shades of green.</p

Evaluation of FDR behavior and peptide degeneracy using different databases.

<p>A) Diagram plotting the number of peptides (left) and PSMs (right) identified with each database as a function of FDR thresholds based on the Percolator <i>q</i>-values. B) Bar graph showing the percentage increment in peptide (left) and PSM (right) identifications achieved with each database when increasing the FDR threshold from 1 to 5%. C) Bar graph illustrating the percentage of shared peptides (left) and PSMs (right) identified with each database at FDR<1%.</p

Schematic illustration of the study workflow.

<p>A) Experimental design. B) Database classes examined.</p

Microorganisms used in this study.

<p>Microorganisms used in this study.</p