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

Additional file 3: Figure S3. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

Complementarity between reads-based database and other database types. Venn diagrams indicate overlap among non-redundant peptide identifications obtained using different sequence databases (see Additional file 1: Figure S1 and Additional file 2: Figure S2 for further details). Percentage increase related to the use of a given database in addition to the counterpart is shown on the bottom, on the same side and in the same color. Peptide identifications were obtained at 5 % FDR, using three different bioinformatic platforms (left, MetaProteomeAnalyzer; middle, MaxQuant; right, Proteome Discoverer) with the corresponding (and above indicated) search engines and peptide validation tools. (TIF 1780 KB

Additional file 1: Figure S1. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

(A) The influence of the ORF finding approach on metagenomic databases. Venn diagrams indicate overlap among non-redundant peptide identifications obtained using ORFs found by FragGeneScan (FGS) or six-frame translation (6FT) as sequence databases. Percentage increase related to the use of a 6FT database in addition to the corresponding FGS database is shown on the bottom-right of each Venn diagram. Metagenomic databases derive from a 6-Mbps metagenome. Peptide identifications were obtained at 5 % FDR, using three different bioinformatic platforms (left, MetaProteomeAnalyzer; middle, MaxQuant; right, Proteome Discoverer) with the corresponding (and above indicated) search engines and peptide validation tools. (B) Comparison between reads- and contigs-based metagenomic databases at different sequencing depths. Venn diagrams indicate overlap among non-redundant peptide identifications obtained using reads (R) or assembled contigs (C) as sequence databases. ORFs from both reads and contigs were found by FragGeneScan (F). Metagenome sequencing depth was 18 (top), 6 (middle), or 3 (bottom) Mbps. Percentage increase related to the use of contigs-based database in addition to the corresponding reads-based database is shown in red on the bottom-right of each Venn diagram, while percentage increase related to the use of reads-based database in addition to the corresponding contigs-based database is shown in blue on the bottom-left of each Venn diagram. Peptide identifications were obtained at 5 % FDR, using three different bioinformatic platforms (left, MetaProteomeAnalyzer; middle, MaxQuant; right, Proteome Discoverer) with the corresponding (and above indicated) search engines and peptide validation tools. (TIF 2050 KB

Additional file 6: Figure S6. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

Cladograms illustrating human gut microbiota taxa detected with differential abundance when using different sequence databases. Spectral counting data obtained upon Sequest-HT/Percolator search (1 % FDR) and MEGAN (left) or Unipept (right) lowest common ancestor (LCA) taxonomic classification were uploaded into LEfSe for differential analysis and cladogram construction. Taxa with higher abundance related to a given database are marked with the specific database color (dark blue, reads-based MG-DB; red, contigs-based MG-DB; turquoise, UniProt Bacteria). (TIF 3450 KB

Additional file 5: Figure S5. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

Histograms illustrating the number of taxa identified with the three DBs in human (left) and mouse (right) samples, distinguishing qualitatively differential (black), quantitatively differential (white) and non-differential (grey) taxa. (TIF 648 KB

Additional file 7: Figure S7. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

Cladograms illustrating mouse gut microbiota taxa detected with differential abundance when using different sequence databases. Spectral counting data obtained upon Sequest-HT/Percolator search (1 % FDR) and MEGAN (left) or Unipept (right) lowest common ancestor (LCA) taxonomic classification were uploaded into LEfSe for differential analysis and cladogram construction. Taxa with higher abundance related to a given database are marked with the specific database color (dark blue, reads-based MG-DB; red, contigs-based MG-DB; turquoise, UniProt Bacteria). (TIF 7330 KB

Additional file 9: Dataset S1. of The impact of sequence database choice on metaproteomic results in gut microbiota studies

Lists of protein families detected with significantly varying abundance when using different DB types in human and mouse samples. (XLSX 28.0 KB