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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

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Related Experiment Video

Updated: May 12, 2026

A Femtoliter Droplet Array for Massively Parallel Protein Synthesis from Single DNA Molecules
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Published on: June 20, 2020

BLAST output visualization in the new sequencing era.

Ralf Stefan Neumann, Surendra Kumar, Kamran Shalchian-Tabrizi

    Briefings in Bioinformatics
    |April 23, 2013
    PubMed
    Summary
    This summary is machine-generated.

    New bioinformatics tools are needed to handle large Basic Local Alignment Search Tool (BLAST) outputs, especially for biologists without extensive computational training. This review categorizes and evaluates freely available software for processing and visualizing BLAST results.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • The Basic Local Alignment Search Tool (BLAST) is a fundamental bioinformatics algorithm with widespread use in biological research.
    • Advancements in sequencing technologies and expanding genomic databases lead to increasingly large BLAST outputs, often exceeding manual processing capabilities.
    • A growing number of biologists entering the field lack specialized bioinformatics expertise, creating a demand for accessible data analysis tools.

    Purpose of the Study:

    • To review and categorize freely available software designed for processing and visualizing Basic Local Alignment Search Tool (BLAST) output.
    • To evaluate these tools based on user-friendliness, analytical functionalities, and capacity for high-throughput data analysis.
    • To address the need for accessible solutions for biologists managing large-scale BLAST results.

    Main Methods:

    • Categorization of software into distinct functional groups: BLAST output interpreters, environments, parsers, and specialized tools.
    • Evaluation of each tool's usability, feature set for data analysis, and performance with large datasets.
    • Literature review and assessment of existing bioinformatics software for BLAST output management.

    Main Results:

    • Identification and classification of various freely available programs for handling BLAST outputs.
    • Assessment of the strengths and weaknesses of different tool categories concerning user experience and analytical power.
    • Highlighting tools suitable for both novice users and high-throughput research environments.

    Conclusions:

    • User-friendly software solutions are crucial for enabling biologists to effectively analyze large BLAST outputs.
    • The reviewed tools offer diverse capabilities, catering to different user needs and research scales.
    • Continued development of accessible bioinformatics tools is essential for advancing biological discovery.