Author: gene_x

Abstract: 1. Integration and analysis of multi-omics data: Explore the integration of different types of omics data (e.g., genomics, transcriptomics, proteomics) from public repositories to uncover novel biolog

Author: gene_x

Abstract: 1. generate_promter_sequences #!/usr/bin/env python3 #./1_generate_promoter_sequences.py gencode.v43.annotation.gtf.db import gffutils from pyfaidx import Fasta import argparse from Bio i

Author: gene_x

Abstract: >http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone Antibody: - CBP (SC-369) - H3K4me1 - H3K4me2 - H3K4me3 - H3K9ac - H3K9me1 - H3K9me3 - H3K27ac - H3K27me3 - H3K36me3 - H3K79me

Author: huang

Abstract: 1. Gonna 將會 Gonna 是 (be) going to 的非正式用法，表示「某人將會／將要做某事」的意思，常見於口語對話或是流行歌曲中，是一種比較輕鬆的表達方式，其句型為「主詞＋be 動詞＋gonna＋原形動詞」。 A: Dylan, do you have any plans for Chinese New Year ? A：Dylan，你農曆年有沒有什麼計畫？ B: Oh

Author: gene_x

Abstract: To find the closest peaks in the genome regions defined by a bed file, you can use a tool like BEDTools. BEDTools provides a function `closest` which allows you to find the closest feature in a second

Author: gene_x

Abstract: If you have genomic coordinates (like gene positions, SNP positions etc) in hg19 and want to convert them to hg38, you'd use what's known as a "liftover". The UCSC Genome Browser provides a tool speci

Author: gene_x

Abstract: import pprint import argparse import matplotlib.pyplot as plt import pandas as pd import gffutils import numpy as np #db = gffutils.create_db('gencode.v43.annotation.gtf', dbfn='gencode.v43.an

Author: gene_x

Abstract: To implement the clustering of promoter types based on motif frequency and distribution using Python, you can follow these steps: 1. Import the required libraries: import pandas as pd import num

Author: gene_x

Abstract: import os ####################################################### ############### Snakefile Configuration ############### ####################################################### configfile: "ba

Author: gene_x

Abstract: 单细胞RNA测序数据分析的具体步骤包括以下几个阶段： 1. 数据预处理：这一步涉及到对原始测序数据进行质量控制，包括移除低质量的测序读段，对读段进行修剪，以及对可能的污染序列进行识别和移除。这一步骤是为了确保后续的分析基于的是高质量的数据。 2. 比对和定量：接下来的步骤是将预处理后的读段比对到参考基因组上，并且对每个细胞中每个基因的表达量进行定量。比对可以使用如STAR, HISAT2等工具