Unknown

Dataset Information

0

A two-step strategy for constructing specifically self-subtracted cDNA libraries.


ABSTRACT: We have developed a new strategy for producing subtracted cDNA libraries that is optimized for connective and epithelial tissues, where a few exceptionally abundant (super-prevalent) RNA species account for a large fraction of the total mRNA mass. Our method consists of a two-step subtraction of the most abundant mRNAs: the first step involves a novel use of oligo-directed RNase H digestion to lower the concentration of tissue-specific, super-prevalent RNAs. In the second step, a highly specific subtraction is achieved through hybridization with probes from a 3'-end ESTs collection. By applying this technique in skeletal muscle, we have constructed subtracted cDNA libraries that are effectively enriched for genes expressed at low levels. We further report on frequent premature termination of transcription in human muscle mitochondria and discuss the importance of this phenomenon in designing subtractive approaches. The tissue-specific collections of cDNA clones generated by our method are particularly well suited for expression profiling.

SUBMITTER: Laveder P 

PROVIDER: S-EPMC113861 | biostudies-literature | 2002 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

A two-step strategy for constructing specifically self-subtracted cDNA libraries.

Laveder Paolo P   De Pittà Cristiano C   Toppo Stefano S   Valle Giorgio G   Lanfranchi Gerolamo G  

Nucleic acids research 20020501 9


We have developed a new strategy for producing subtracted cDNA libraries that is optimized for connective and epithelial tissues, where a few exceptionally abundant (super-prevalent) RNA species account for a large fraction of the total mRNA mass. Our method consists of a two-step subtraction of the most abundant mRNAs: the first step involves a novel use of oligo-directed RNase H digestion to lower the concentration of tissue-specific, super-prevalent RNAs. In the second step, a highly specific  ...[more]

Similar Datasets

| S-EPMC3105984 | biostudies-literature
| S-EPMC1383735 | biostudies-literature
| S-EPMC3636440 | biostudies-literature
2014-04-10 | GSE37153 | GEO
| S-EPMC8709999 | biostudies-literature
| S-EPMC4576280 | biostudies-literature
| S-EPMC310898 | biostudies-literature
| S-EPMC4229100 | biostudies-literature
| S-EPMC8163216 | biostudies-literature
| S-EPMC6861333 | biostudies-literature