Results 31 to 40 of about 5,890,604 (383)

Genomics and proteomics: a signal processor's tour [PDF]

open access: yes, 2004
The theory and methods of signal processing are becoming increasingly important in molecular biology. Digital filtering techniques, transform domain methods, and Markov models have played important roles in gene identification, biological sequence ...
Vaidyanathan, P. P.
core   +2 more sources

Sequencing Enabling Design and Learning in Synthetic Biology

open access: yesCurrent Opinion in Chemical Biology, 2020
The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design.
Gilliot, Pierre-Aurelien M A   +1 more
openaire   +4 more sources

Lineage-specific interface proteins match up the cell cycle and differentiation in embryo stem cells

open access: yesStem Cell Research, 2014
The shortage of molecular information on cell cycle changes along embryonic stem cell (ESC) differentiation prompts an in silico approach, which may provide a novel way to identify candidate genes or mechanisms acting in coordinating the two programs. We
Angela Re   +4 more
doaj   +1 more source

Developing and applying heterogeneous phylogenetic models with XRate [PDF]

open access: yes, 2012
Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features.
A Heger   +32 more
core   +5 more sources

Quantification of within-sample genetic heterogeneity from SNP-array data

open access: yesScientific Reports, 2017
Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement.
Pierre Martinez   +5 more
doaj   +1 more source

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

open access: yesMolecular biology and evolution, 2013
We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained ...
K. Katoh, D. Standley
semanticscholar   +1 more source

Decoding Sequence Classification Models for Acquiring New Biological Insights [PDF]

open access: yes, 2010
Classifying biological sequences is one of the most important tasks in computational biology. In the last decade, support vector machines (SVMs) in combination with sequence kernels have emerged as a de-facto standard.
Andreas Kothmeier   +5 more
core   +2 more sources

ANALYZING THE GENOMIC VARIATION OF MICROBIAL CELL FACTORIES IN THE ERA OF “NEW BIOTECHNOLOGY”

open access: yesComputational and Structural Biotechnology Journal, 2012
The application of genome-scale technologies, both experimental and in silico, to industrial biotechnology has allowed improving the conversion of biomass-derived feedstocks to chemicals, materials and fuels through microbial fermentation. In particular,
Markus Herrgård, Gianni Panagiotou
doaj   +1 more source

Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega

open access: yesMolecular Systems Biology, 2011
Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets
Fabian Sievers   +11 more
semanticscholar   +1 more source

Are there laws of genome evolution? [PDF]

open access: yes, 2011
Research in quantitative evolutionary genomics and systems biology led to the discovery of several universal regularities connecting genomic and molecular phenomic variables.
Koonin, Eugene V.
core   +4 more sources

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