Advanced Interdisciplinary Projects Lab(AIPLab) 討論區

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 於: 今天 02:22:46 am 
發表者 admin - 最新文章 由 admin
GO:0043312 neutrophil degranulation

 於: 四月 24, 2017, 11:12:12 pm 
發表者 admin - 最新文章 由 admin
程式碼: [Select]
with open(ggname, 'r') as csvfile:
     spamreader = csv.reader(csvfile, delimiter=',')
     for row in spamreader:
         print (', '.join(row))

Merge GO genes with DE columns
程式碼: [Select]
import sys
import csv

Usage: CheckPathway genes.txt associations.tsv output.csv
goname = 'associations.tsv'
ggname = 'SRP073050-de.csv'
mmname = 'merge.csv'
print ('Number of arguments:', len(sys.argv), 'arguments.')
print ('Argument List:', str(sys.argv))
if len(sys.argv) > 1:
    goname = sys.argv[1]
if len(sys.argv) > 2:
    ggname = sys.argv[2]
if len(sys.argv) > 3:
    mmname = sys.argv[3]

genes = dict()
inset = list()
noset = list()
mmset = list();
with open(ggname, 'r') as csvfile:
     genereader = csv.reader(csvfile, delimiter=',')
     for row in genereader:
         #print (', '.join(row))
         if row[0] not in genes:
            genes[row[0]] = row
empty = ['0', 'NA', 'NA']
title = ['baseMean', 'log2FoldChange', 'p-value']
first = True
with open(goname, 'r') as tsvfile:
     goreader = csv.reader(tsvfile, delimiter='\t')
     for row in goreader:
        if first:
           first = False
           #print (', '.join(row))
           gname = row[2]
           if gname in genes:

with open(mmname, 'w') as csvfile2:
    mmwriter = csv.writer(csvfile2, delimiter=',', lineterminator='\n')
    for row in mmset:

 於: 四月 24, 2017, 09:54:13 pm 
發表者 admin - 最新文章 由 admin
import os
print os.getcwd()

 於: 四月 24, 2017, 04:28:07 am 
發表者 admin - 最新文章 由 admin
Analysis of the Human Tissue-specific Expression

 於: 四月 24, 2017, 02:04:23 am 
發表者 admin - 最新文章 由 admin
Prediction and Quantification of Splice Events from RNA-Seq Data
Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package

 於: 四月 24, 2017, 01:43:34 am 
發表者 admin - 最新文章 由 admin

 於: 四月 23, 2017, 11:11:50 pm 
發表者 admin - 最新文章 由 admin
Transcriptome analysis of the oriental melon (Cucumis melo L. var. makuwa) during fruit development

The molecular bases of floral scent evolution under artificial selection: insights from a transcriptome analysis in Brassica rapa
Three of the genes showed significant up-regulation in the high lines while the other two showed no significant difference between high and low lines (Table 2). Thus, these three genes are probably the functional genes encoding the PAAS in Brassica rapa. In addition to the actual PAAS gene, several genes in the shikimate pathway which synthesizes phenylalanine as the substrate of PAA synthesis, also showed increased expression in high line plants. Specifically, genes in four of the six reactions from shikimate to phenylalanine showed increased expression (Fig. 1). On the contrary, there are only three reactions upstream of shikimate that showed decreased expression in two corresponding genes: 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) synthase and bifunctional 3-dehydroquinate dehydratase (DHD)–shikimate dehydrogenase (SDH). DAHP expression was shown to be induced upon wounding in Solanaceae31 and methyl jasmonate treatment in Arabidopsis32. However, we know little about the reason for the down-regulation of those genes; feedback of phenylalanine and other intermediate product accumulation in the plants may play a role in this response.

 於: 四月 23, 2017, 01:59:26 am 
發表者 admin - 最新文章 由 admin
(1) Next generation sequencing
Next generation sequencing in cancer research and clinical application

(2) RNA-Seq

(3) Bioinformatics tools

 於: 四月 19, 2017, 04:09:27 pm 
發表者 admin - 最新文章 由 admin
Now includes comprehensive GO annotations directly imported from the GO database

 於: 四月 18, 2017, 09:35:22 pm 
發表者 admin - 最新文章 由 admin
MYC proto-oncogene, bHLH transcription factor
The protein encoded by this gene is a multifunctional, nuclear phosphoprotein that plays a role in cell cycle progression, apoptosis and cellular transformation. It functions as a transcription factor that regulates transcription of specific target genes. Mutations, overexpression, rearrangement and translocation of this gene have been associated with a variety of hematopoietic tumors, leukemias and lymphomas, including Burkitt lymphoma. There is evidence to show that alternative translation initiations from an upstream, in-frame non-AUG (CUG) and a downstream AUG start site result in the production of two isoforms with distinct N-termini. The synthesis of non-AUG initiated protein is suppressed in Burkitt's lymphomas, suggesting its importance in the normal function of this gene.

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