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運用Python解生物資訊問題(3)

練習1

先下載herpesvirus_genome.json
herpesvirus_genome.json

A)

Find the frequency of each amino-acid in the herpesvirus's proteome.
找出herpesvirus的蛋白質體中，所有胺基酸出現的頻率。

import os
import json
from collections import Counter
# r代表raw python string literal, 不用正規表達式表示。
DIR = r'C://downloads'

f = open(os.path.join(DIR, 'herpesvirus.json'), 'r')

data = json.load(f)

f.close()

# 將計算好的氨基酸出現次數除以總胺基酸數
def normalize(counter):
    total = sum(counter.values())
    return {key: count / total for key, count in counter.items()}

# 建立空的記數字典
all_aa_counter = Counter()
# 這邊可以先另外用Notepad++開herpesvirus.json檔案看
# coding regions以list儲存每個coding region資訊
# 其中每個coding region有個key值為translation
# 當中儲存該coding region的胺基酸序列
for coding_region in data['coding_regions']:
   # 用update字典的方法加入計數器 
   aa_aa_counter.update(coding_region['translation']
)

aa_all_freq = normalize(all_aa_counter)
print(all_aa_freq)
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這裡是Eric Juo(小卓)無聊發起的小小空間，目的在於共享生活中學習到的知識及經驗，這裡是一個新手友善空間，不要求筆記寫的多完整，只希望學習到的東西可以在某個地方保存下來，並可以幫助其他人一起學習
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RNA-Seq Analysis

RNA-Seq stands for RNA sequencing. It is a technology for studying RNA species. In a cell, the majority of RNA sepcies is ribosomal RNA (rRNA) which accounts for 90% of total RNA. Conversly, messenger RNA (mRNA) only aocunts for 1-2% of total RNA. The central dogma of biology tells us that DNA transcribes into mRNA and then mRNA translates into protein to execute biological functions. mRNA is the only RNA species we know encodes protein seuqences. Studying the level of mRNAs enables us to understand the gene expression level and infer the protein expression level. Therefore, most of time we only interest in the level of mRNA, and wants to deplet rRNA from the total RNA. Preprocessing RNA samples before sequencing includes total RNA extraction, mRNA enrichment, fragmentation, first-strand cDNA synthesis, RNA strand digestion, second-strand cDNA synthesis, 3'end repair, adenylation and adatper ligation. In total RNA extraction step, extracted RNA should not have signs of degradation. In mRNA enrichment, poly-T beads or poly-T columns are used to separate mRNA from other RNA sepcies, since only mRNA contains poly-A tails. mRNAs are fragmented by chemicals or ultrasonic into certain size range that fits for the sequencing capacity of sequencer. mRNA are primed with random hexamer and reverse-transcribed into first-strand cDNA, followed by RNA strand digestion and second-strand cDNA synthesis. For making strand-specific RNA-seq, TTPs are replaced with dUTPs, which serves as marks for the second-strand, in the second-strand cDNA synthesis step. Since the nature of polymerase synthesis, the double strand cDNA product lose several bases at the 3' ends on each strand. The 3'ends are repaired by dna end repair enzyme and then added a adenine. Y-shape adapters with 3'-T overhang are ligated to the 3'-A overhang of the double strand cDNA. To reduce the complexity in short read assembly, dUTP marked cDNA stands are digested using uracil-N-glycolas (UNG). The single strand cDNAs are then subjected to sequencing. In sequencing step, primer targeting adapter sequence aneal to one end of cDNA. Fluoresence-labelled ATPs, CTPs, GTPs or TTPs is added one at a time and photographed. Layers of photographes are analyzied by computer to interpret which nucleotide is added to the growing strand in each reaction. For paired end sequencing, the other primer targeting adatper on the other side is added and squenced again. The resultant are 2 separate files, one from forward strand, the other from reverse strand. Raw reads obtained from sequencer should undergo quality control before downtream analysis. Raw reads data are normally reported in fastq format. Fastq format stores 4 lines of information for each read. The first line is read name, the second line is sequence, the thrid line is a "+" sign, and the forth line is ASCII characters encypting qualtiy scores. For illumina sequencer, the quality socre is reported in Phred33 scheme. To obtain numberical quality score, ASCII characters need to be converted to numbers they represented in computer and minus 33. Quality score is used to repersent how confidence we can say this base is called correctly. A 40 quality score means 1 in 10000 chances this base is called incorrectly. Thus, higher quality score indicating higher chance this base is called correctely. Usually, we would want to trim off bases with quality score lower than 20. Low quality score base calling usually happens at the 3' end of reads since the polymerase is getting weak attaching to the template and prone to add wrong nucleic acids. FastQC is a commonly used tool to give an overview of read quality. It reports per base quality, per sequence quality, per base nucleotide content, per sequence GC content, number of duplicated sequences, overrepresented sequences and adapter content metrices. Per base quality metric shows the distribution of quality score in box plot for each base location. The upper boundary of the yellow box is the third quantile of qualtiy scores, and the lower boundary of the box is the first quantile. The red line at the middle of box is the median value of quality scores. Per sequence quality metric averages base quality for each read and shows the distribution of per read quality. For good quality reads, the distribution of read quality should skews toward high quality side. Per base nucleotide content metric shows the frequencies of nucleic acid appear at each base location. In principle, RNA has been randomly fragmented, so the frequency of each nucleic acid should distribute evenly across all read length. However, RNA-Seq data is an exception, a research has found that priming with random hexamer in cDNA synthesis step seems to have certain selection for fragmented RNA sequences, which then results in not that random nucleic acid content at the 5'end of reads. 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