Evidence map›Paper›PMID 27071032›Full record

ArticlePloS one2016

CisSERS: Customizable In Silico Sequence Evaluation for Restriction Sites.

Richard M Sharpe, Tyson Koepke, Artemus Harper, John Grimes, Marco Galli, Mio Satoh-Cruz, Ananth Kalyanaraman, Katherine Evans, David Kramer, Amit Dhingra

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Richard M SharpeMolecular Plant Sciences Graduate Program, Washington State University, Pullman, Washington, United States of America.
Tyson KoepkeMolecular Plant Sciences Graduate Program, Washington State University, Pullman, Washington, United States of America.
Artemus HarperDepartment of Horticulture, Washington State University, Pullman, Washington, United States of America.
John GrimesElectrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States of America.
Marco GalliDepartment of Horticulture, Washington State University, Pullman, Washington, United States of America.
Mio Satoh-CruzMSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America.
Ananth KalyanaramanElectrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States of America.
Katherine EvansDepartment of Horticulture, Washington State University, Pullman, Washington, United States of America.
David KramerMSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America.
Amit DhingraMolecular Plant Sciences Graduate Program, Washington State University, Pullman, Washington, United States of America.

Funding

TRAINING IN BIOTECHNOLOGY: EMPHASIS ON PROTEIN CHEMISTRYT32GM008336 · NIGMS · WASHINGTON STATE UNIVERSITY · PI CALL, DOUGLAS R. · 1989 to 2023
$8.9M
NIGMS NIH HHS T32 GM008336NIGMS NIH HHS T32GM008336
6 · The paper itself

Abstract

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated to enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERS enable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3'UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERS and results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.

Indexed as

3' Untranslated RegionsComputational BiologyComputer SimulationGenomeGenomicsGenotypeHumansNucleotide MotifsPolymorphism, GeneticSequence Analysis, DNASoftwareUser-Computer Interface3' Untranslated Regions

Identifiers

PMID27071032
PMCPMC4829253

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.