Milí kolegové a kolegyně,
chtěli bychom vás srdečně pozvat na poslední přednášku semináře v tomto
semestru, která se koná již zítra, tj. ve středu 17.5.2017, od 17:20 v
posluchárně S4.
Hostem na semináři bude Karel Fišer z 2. lékařské fakulty Univerzity
Karlovy a téma přednášky bude
Nadgenomika v krvi
Těšíme se srdečně na vaši účast!
Za všechny organizátory semináře s pozdravem,
Petr Daněček
Stránky semináře
http://bioinformatika.mff.cuni.cz/seminar/
--------------------------------------
Karel Fišer
Nadgenomika v krvi
Epigenomika je oblast genetiky popisující mechanismy regulace genové
exprese "nad" DNA. Několik nedávných projektů rozsahem srovnatelných se
sekvenací lidského genomu umožnilo popsat epigenetické modifikace
napříč typy krevních elementů. Do vztahu tak můžeme dát epigenetický
stav chromatinu genů charakteristických pro buněčné typy, jejich
regulátorů a míst vazeb těchto regulátorů.
Milí kolegové a kolegyně,
chtěli bychom vás srdečně pozvat na další přednášku semináře, která se
koná ve středu 3.5.2017, od 17:20 v posluchárně S4.
Hostem na semináři bude Karel Sedlář z Ústavu biomedicínského
inženýrství na VUT v Brně a téma přednášky bude
Visualizations in metagenomics
Těšíme se srdečně na vaši účast!
Za všechny organizátory semináře s pozdravem,
Petr Daněček
Stránky semináře
http://bioinformatika.mff.cuni.cz/seminar/
--------------------------------------
Karel Sedlář
Visualizations in metagenomics
One of main steps in a study of microbial communities is resolving
their composition, diversity and function. In the past, these issues
were mostly addressed by the use of amplicon sequencing of a target
gene because of reasonable price and easier computational
postprocessing of the bioinformatics data. With the advancement of
sequencing techniques, also whole metagenome shotgun sequencing became
popular, allowing much more detailed analysis of the metagenomics data,
including reconstruction of novel microbial genomes and to gain
knowledge about genetic potential and metabolic capacities of whole
environments. Clear and easy-to-understand visualization plays an
important role within both of these sequencing approaches.
Amplicon sequencing of a barcode sequence, e.g. bacterial 16S rRNA
gene, provides an easy way to perform qualitative, as well as
quantitative analysis of a microbial community. A wide range of
techniques can be used to visualize datasets represented by relative
abundances of OTUs (Operational Taxonomic Units) in different samples.
Dimensionality reduction techniques, e.g. PCoA (Principal Coordinate
Analysis) using UniFrac metrics, provides informative visualization
showing relations between samples without a need for identification of
OTUs against a reference database. Bipartite graphs and other network
visualizations, on the other hand, highlight the differences and
similarities among samples by showing unique and shared taxa. At last,
not at least, clustered heatmaps of correlation coefficient can show
relations among microbiome composition and other parameters, e.g.
presence of antibiotic resistance genes.
Visualization in whole metagenome shotgun sequencing approach often
accompanies clustering of sequences, so called binning. Since lot of
techniques perform clustering in unsupervised manner, the use of
interactive inspection and visualization can be suitable for validity
verification of the binning output or even for manual fine-tune of the
automatic clustering. The visualization methods use information
regarding sequence composition or coverage to produce coordinates in
two- or three-dimensional space to describe the given sequence. In some
cases, the sequences can be visualized simply by the use of the
parameters, e.g. OFDEG (Oligonucleotide Frequency Derived Error
Gradient), GC content and others, as coordinates without any further
transformation. In the majority of cases, however, the sequences are
described by more than three parameters, disabling them from being
projected into a humanly comprehensible space. Denouement is then
brought by use of dimensionality reduction techniques, such as PCA
(Principal Component Analysis), SOM (Self-Organizing Maps), or t-SNE
(t-distributed Stochastic Neighbor Embedding).