scholarly journals Using Sequence Constraints for Modelling Network Interactions

2020 ◽  
pp. 3-13
Author(s):  
Johannes De Smedt ◽  
Junichiro Mori ◽  
Masanao Ochi
Keyword(s):  
Sequence Constraints

scholarly journals DNA Sequence Constraints Define Functionally Active Steroid Nuclear Receptor Binding Sites in Chromatin

Endocrinology ◽  
2017 ◽  
Vol 158 (10) ◽  
pp. 3212-3234 ◽  
Author(s):  
Laurel A Coons ◽  
Sylvia C Hewitt ◽  
Adam B Burkholder ◽  
Donald P McDonnell ◽  
Kenneth S Korach
Keyword(s):  
Dna Sequence ◽  
Nuclear Receptor ◽  
Receptor Binding ◽  
Binding Sites ◽  
Sequence Constraints

scholarly journals HEDGES error-correcting code for DNA storage corrects indels and allows sequence constraints

2020 ◽  
Vol 117 (31) ◽  
pp. 18489-18496 ◽  
Author(s):  
William H. Press ◽  
John A. Hawkins ◽  
Stephen K. Jones ◽  
Jeffrey M. Schaub ◽  
Ilya J. Finkelstein
Keyword(s):  
Dna Synthesis ◽  
Large Scale ◽  
Broad Class ◽  
Error Correcting Code ◽  
Gc Content ◽  
Information Storage ◽  
High Rate ◽  
Information Encoding ◽  
Outer Code ◽  
Sequence Constraints

Synthetic DNA is rapidly emerging as a durable, high-density information storage platform. A major challenge for DNA-based information encoding strategies is the high rate of errors that arise during DNA synthesis and sequencing. Here, we describe the HEDGES (Hash Encoded, Decoded by Greedy Exhaustive Search) error-correcting code that repairs all three basic types of DNA errors: insertions, deletions, and substitutions. HEDGES also converts unresolved or compound errors into substitutions, restoring synchronization for correction via a standard Reed–Solomon outer code that is interleaved across strands. Moreover, HEDGES can incorporate a broad class of user-defined sequence constraints, such as avoiding excess repeats, or too high or too low windowed guanine–cytosine (GC) content. We test our code both via in silico simulations and with synthesized DNA. From its measured performance, we develop a statistical model applicable to much larger datasets. Predicted performance indicates the possibility of error-free recovery of petabyte- and exabyte-scale data from DNA degraded with as much as 10% errors. As the cost of DNA synthesis and sequencing continues to drop, we anticipate that HEDGES will find applications in large-scale error-free information encoding.

scholarly journals Conserved regions in long non-coding RNAs contain abundant translation and protein–RNA interaction signatures

2019 ◽  
Vol 1 (1) ◽  
pp. e2-e2 ◽  
Author(s):  
Jorge Ruiz-Orera ◽  
M Mar Albà
Keyword(s):  
Biological Significance ◽  
Ribosome Profiling ◽  
Evolutionary Analysis ◽  
Amino Acid Sequence Level ◽  
Protein Coding ◽  
Conserved Regions ◽  
Non Coding Rnas ◽  
Rna Interaction ◽  
Regulatory Functions ◽  
Sequence Constraints

Abstract The mammalian transcriptome includes thousands of transcripts that do not correspond to annotated protein-coding genes and that are known as long non-coding RNAs (lncRNAs). A handful of lncRNAs have well-characterized regulatory functions but the biological significance of the majority of them is not well understood. LncRNAs that are conserved between mice and humans are likely to be enriched in functional sequences. Here, we investigate the presence of different types of ribosome profiling signatures in lncRNAs and how they relate to sequence conservation. We find that lncRNA-conserved regions contain three times more ORFs with translation evidence than non-conserved ones, and identify nine cases that display significant sequence constraints at the amino acid sequence level. The study also reveals that conserved regions in intergenic lncRNAs are significantly enriched in protein–RNA interaction signatures when compared to non-conserved ones; this includes sites in well-characterized lncRNAs, such as Cyrano, Malat1, Neat1 and Meg3, as well as in tens of lncRNAs of unknown function. This work illustrates how the analysis of ribosome profiling data coupled with evolutionary analysis provides new opportunities to explore the lncRNA functional landscape.

scholarly journals Phenotypes can be robust and evolvable if mutations have non-local effects on sequence constraints

2018 ◽  
Vol 15 (138) ◽  
pp. 20170618 ◽  
Author(s):  
Marcel Weiß ◽  
Sebastian E. Ahnert
Keyword(s):  
Rna Secondary Structure ◽  
Local Effects ◽  
Protein Coding ◽  
Diverse Range ◽  
Positive Correlation ◽  
Alternative Phenotypes ◽  
Genetic Sequences ◽  
Non Local ◽  
Sequence Constraints ◽  
Sequence Position

The mapping between biological genotypes and phenotypes plays an important role in evolution, and understanding the properties of this mapping is crucial to determine the outcome of evolutionary processes. One of the most striking properties observed in several genotype–phenotype (GP) maps is the positive correlation between the robustness and evolvability of phenotypes. This implies that a phenotype can be strongly robust against mutations and at the same time evolvable to a diverse range of alternative phenotypes. Here, we examine the causes for this positive correlation by introducing two analytically tractable GP map models that follow the principles of real biological GP maps. The first model is based on gene-like GP maps, reflecting the way in which genetic sequences are organized into protein-coding genes, and the second one is based on the GP map of RNA secondary structure. For both models, we find that a positive correlation between phenotype robustness and evolvability only emerges if mutations at one sequence position can have non-local effects on the sequence constraints at another position. This highlights that non-local effects of mutations are closely related to the coexistence of robustness and evolvability in phenotypes, and are likely to be an important feature of many biological GP maps.

Sign in / Sign up

Export Citation Format

Share Document