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Cell Lines

Cell Lines

A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...

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Updated: Jun 6, 2026

A Simple Red Blood Cell Lysis Method for the Establishment of B Lymphoblastoid Cell Lines

A Simple Red Blood Cell Lysis Method for the Establishment of B Lymphoblastoid Cell Lines

Published on: January 14, 2017

Blasted cell line names.

Jing Wang¹, Lauren A Byers, John S Yordy

¹Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.

Cancer Informatics

|November 18, 2010

Summary

This summary is machine-generated.

Inconsistent cell line sample names across datasets hinder integration. Applying the Needleman-Wunsch algorithm offers a simple, effective solution for accurate sample name matching.

Keywords:

Blast Cancer cell lines

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Area of Science:

Bioinformatics
Computational Biology
Data Integration

Background:

Inconsistent sample naming conventions, particularly with punctuation and spacing, complicate the integration of diverse biological datasets.
Variations often occur at the junction of alphabetic and numeric components within cell line names.

Purpose of the Study:

To develop a robust method for harmonizing cell line sample names from disparate data sources.
To address the challenge of data integration caused by naming inconsistencies.

Main Methods:

Implementation of a simplified Needleman-Wunsch global sequence alignment algorithm.
Application of the algorithm to compare and match cell line names across different datasets.

Main Results:

The Needleman-Wunsch algorithm successfully identified all correct sample name matches.
Incorrect matches were minimized and characterized by significantly lower alignment scores.
The algorithm effectively distinguished true matches from spurious ones, even with minor naming variations.

Conclusions:

A straightforward application of the Needleman-Wunsch algorithm serves as an effective initial step for matching sample names.
This method facilitates more reliable data integration by standardizing sample identifiers.