Dataset Information

Principal components analysis of protein sequence clusters.

ABSTRACT: Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large variable space, i.e. the number of amino acids that make up the protein sequence, and PCA is powerful at reducing the dimensionality of complex problems by projecting the data into an eigenspace that represents the directions of greatest variation. However, PCA of aligned protein sequence families is complicated by the fact that protein sequences are traditionally represented by single letter alphabetic codes, whereas PCA of protein sequence families requires conversion of sequence information into a numerical representation. Here, we introduce a new amino acid sequence conversion algorithm optimized for PCA data input. The method is demonstrated using a small artificial dataset to illustrate the characteristics and performance of the algorithm, as well as a small protein sequence family consisting of nine members, COG2263, and finally with a large protein sequence family, Pfam04237, which contains more than 1,800 sequences that group into two sub-clusters.

SUBMITTER: Wang B

PROVIDER: S-EPMC3982804 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Principal components analysis of protein sequence clusters.

Wang Bo B Kennedy Michael A MA

Journal of structural and functional genomics 20140205 1

Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large v ...[more]

PMID: 24496727

Similar Datasets

Project description:To decipher gene regulatory networks, we used systematic suppression of 97 transcription factors (TFs) and 7 other genes with shRNA in mouse embryonic stem (ES) cells, followed by global gene expression profiling. Meta-analysis of these data together with the earlier data obtained by the induction of 50 TFs and the existing genome-wide data on TF binding sites identified the sets of regulated target genes for 23 TFs. Principal component analysis shows different roles of two groups of TFs that are active in ES cells: Pou5f1 and Sox2 support the expression of their target genes and prevent the upregulation of trophectoderm-related genes, whereas Esrrb, Sall4, Nanog, Gbx2, Grhl2, Mtf2, Aff1, Tcfap4, and Cdc5l support the expression of targets of Esrrb, including glycolysis genes, and prevent upregulation of targets of Trp53 and Polycomb TFs. If TFs from the second group are downregulated while Pou5f1 and Sox2 are still active, then the cell state changes towards epiblast lineages. Sequences for shRNA were designed to target 3 untranslated region of genes (Supplementary Table S8). Gene expression change was checked with real time qPCR (Supplementary Figure S7) and Westerm blot. ES cells (ES[MC1R(20)], passage 20) were cultured without feeders and were co-transfected with 1.6 g of shRNA expression vector and 0.4 g of pPyCAG-EGFP-IP carrying expression cassettes for puromycin resistant genes and EGFP using Effectene (Qiagen). Transfected cells were cultured in presence of 1.5 g/ml of puromycin and were harvested at 72 h after transfection. Mock cells were treated with transfection reagent without DNA and cultured in the absence of puromycin. Experiments were done in 3 replications with 2 of them used for gene expression profiling with microarrays. Total RNA was isolated by TRIzol (Invitrogen) after 2 days. Cy3-CTP labeled sample targets were prepared with total RNA by Low RNA Input Fluorescent Linear Amplification Kit (Agilent). Cy5-CTP labeled reference target was Stratagene Universal Mouse Reference RNA. Note that the processed data in the paper, which is also attached as GSE26520_Table_data.txt.gz, is slightly different from the values columns in each sample. The original processed data are normalized with a batch method, as the new batches of arrays added in the set. The value columns in this submission reflect full normalization as described in the data processing fields in each sample.

Dataset Information

Principal components analysis of protein sequence clusters.

Publications

Principal components analysis of protein sequence clusters.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets