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# biopython_notebook_1.ipynb
Repository: Deya-B/Bioinformatics-notes
<code>
from Bio.Seq import Seq
seq = Seq('GATTACA')
#Seq methods represent biological sequences as strings
print(seq)
</code>
<code>
seq = Seq('CAT')
for base in seq:
print(base, end=' ')
seq + 'GAT'
</code>
<code>
dna = Seq('GATTACA')... | {
"filename": "biopython_notebook_1.ipynb",
"repository": "Deya-B/Bioinformatics-notes",
"query": "transformed_from_existing",
"size": 199361,
"sha": ""
} |
# 02-warmup-sol.ipynb
Repository: hanisaf/mist5730-6380-spring2020
Refer to [the University of Georgia by the Numbers Page](https://www.uga.edu/facts.php)
Reconstruct (most) of this page using markdown in this notebook
# UGA by the Numbers
**Founded:**
> January 27, 1785, by the Georgia General Assembly. UGA is... | {
"filename": "02-warmup-sol.ipynb",
"repository": "hanisaf/mist5730-6380-spring2020",
"query": "transformed_from_existing",
"size": 3939,
"sha": ""
} |
# SIMS_tutorial_4.ipynb
Repository: braingeneers/SIMS
## **SIMS Tutorial**
In this tutorial, we will walk through the [SIMS (Scalable, Interpretable Machine Learning for Single Cell)](https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00165-4) pipeline step by step. SIMS is a deep learning-based tool built on T... | {
"filename": "SIMS_tutorial_4.ipynb",
"repository": "braingeneers/SIMS",
"query": "transformed_from_existing",
"size": 119177,
"sha": ""
} |
# HiDENSEC.ipynb
Repository: songlab-cal/HiDENSEC
# Global Variables & Function Definitions
These global definitions require evaluation before running HiDENSEC on any concrete Hi-C map.
## Modules
<code>
import numpy as np
import scipy.sparse as sp_sparse
import scipy.signal as sp_signal
import scipy.ndimage as sp_... | {
"filename": "HiDENSEC.ipynb",
"repository": "songlab-cal/HiDENSEC",
"query": "transformed_from_existing",
"size": 51034,
"sha": ""
} |
# DESeq2_4.ipynb
Repository: LucaMenestrina/DEGA
# DESeq2 Use Case
## Load Libraries
<code>
library("DESeq2")
library("genefilter")
</code>
Set variables (data from the [Bottomly et al.](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017820) dataset)
<code>
GENE_COUNTS = "https://raw.githubuser... | {
"filename": "DESeq2_4.ipynb",
"repository": "LucaMenestrina/DEGA",
"query": "transformed_from_existing",
"size": 14505,
"sha": ""
} |
# Project_未命名.ipynb
Repository: Peevin/TNBC
<code>
import scanpy as sc
import pandas as pd
import numpy as np
</code>
<code>
sc.settings.set_figure_params(dpi=300, facecolor='white')
</code>
<code>
adata = sc.read_h5ad('/Users/liupeiwen/BC/21 CC Single-cell analyses reveal key immune cell subsets associated with res... | {
"filename": "Project_未命名.ipynb",
"repository": "Peevin/TNBC",
"query": "transformed_from_existing",
"size": 255361,
"sha": ""
} |
# taxonomy_explore_github_topics.ipynb
Repository: kuefmz/define
<code>
import pandas as pd
</code>
<code>
df = pd.read_csv('topics.csv')
</code>
<code>
df.head()
</code>
<code>
df.shape
</code>
<code>
print('Number of different topics on GitHub')
len(df['topic'].unique())
</code>
<code>
topic_counter = {}
for in... | {
"filename": "taxonomy_explore_github_topics.ipynb",
"repository": "kuefmz/define",
"query": "transformed_from_existing",
"size": 48165,
"sha": ""
} |
# bioinformatics_bootcamp_2018_ATAC-seq-checkpoint_2.ipynb
Repository: ryanmarina/BMS
# BIOM 200 bioinformatics bootcamp - ATAC-seq analysis
* [(Pre-class) Introduction](#introduction)
* [(Pre-class) Installations](#installations)
* [(In-class) Data processing](#processing)
* [(In-class) Data analysis](#processing)
*... | {
"filename": "bioinformatics_bootcamp_2018_ATAC-seq-checkpoint_2.ipynb",
"repository": "ryanmarina/BMS",
"query": "transformed_from_existing",
"size": 28407,
"sha": ""
} |
# thesis_homer_genome_annotation_1.ipynb
Repository: liouhy/2022-Charite-master
# HOMER - genome annotation
Here, we used HOMER to annotate genomic regions from scATAC-seq datasets. First, we created bed files of genomic regions.
<code>
import pandas as pd
import anndata as ad
</code>
<code>
# Granja et al.
ft = pd.... | {
"filename": "thesis_homer_genome_annotation_1.ipynb",
"repository": "liouhy/2022-Charite-master",
"query": "transformed_from_existing",
"size": 3038,
"sha": ""
} |
# table_model_1_1.ipynb
Repository: DongjoonLim/EvoLSTM
<code>
import numpy as np
from tqdm.notebook import tqdm
!nvidia-smi
</code>
<code>
k = 7
des = str(np.load('prepData/insert2Des__HPGPNRMPC_hg38_chr2.npy'))
anc = str(np.load('prepData/insert2Anc__HPGPNRMPC_hg38_chr2.npy'))
print(len(anc), len(des))
def build... | {
"filename": "table_model_1_1.ipynb",
"repository": "DongjoonLim/EvoLSTM",
"query": "transformed_from_existing",
"size": 32967,
"sha": ""
} |
# Evaluate_Integration_LISI.ipynb
Repository: pughlab/cancer-scrna-integration
---
# Evaluate data integration using LISI
*L.Richards*
*2021-06-14*
*/cluster/projects/pughlab/projects/cancer_scrna_integration/evalutation/lisi*
---
https://github.com/immunogenomics/LISI
<code>
# install.packages("devtools")
# de... | {
"filename": "Evaluate_Integration_LISI.ipynb",
"repository": "pughlab/cancer-scrna-integration",
"query": "transformed_from_existing",
"size": 5486,
"sha": ""
} |
# MCAsubset-checkpoint.ipynb
Repository: CSUBioGroup/scNCL-release
<code>
%load_ext autoreload
%autoreload 2
import os
import h5py
import seaborn as sns
import numpy as np
import pandas as pd
import scanpy as sc
import anndata
import csv
import gzip
import scipy.io
import scipy.sparse as sps
import matplotlib.pyplot... | {
"filename": "MCAsubset-checkpoint.ipynb",
"repository": "CSUBioGroup/scNCL-release",
"query": "transformed_from_existing",
"size": 199827,
"sha": ""
} |
# GPT_1.ipynb
Repository: ZubairQazi/NDE-GPT
# GPT for Topic Categorization
<code>
import json
import pandas as pd
import numpy as np
import ast
import os
import re
from bs4 import BeautifulSoup
import csv
from tqdm.notebook import tqdm
import openai
from langchain.llms import OpenAI
from langchain.chat_models impo... | {
"filename": "GPT_1.ipynb",
"repository": "ZubairQazi/NDE-GPT",
"query": "transformed_from_existing",
"size": 117418,
"sha": ""
} |
# New_eng_academic_research_2.ipynb
Repository: kdj0712/teamKim1
<code>
import pandas as pd
import numpy as np
</code>
<code>
df_Riss_research = pd.read_csv("./csv/Seleniums.eng_academic_research.csv")
df_Riss_research.drop(labels='_id', axis=1, inplace=True)
df_Riss_research['research_subject']
</code>
## 데이터 전처리
... | {
"filename": "New_eng_academic_research_2.ipynb",
"repository": "kdj0712/teamKim1",
"query": "transformed_from_existing",
"size": 277407,
"sha": ""
} |
# analyses_3.SCENIC-V10-V2_1.ipynb
Repository: aertslab/scenicplus
### 1. Create SCENIC+ object
<code>
# Load functions
from scenicplus.scenicplus_class import SCENICPLUS, create_SCENICPLUS_object
from scenicplus.preprocessing.filtering import *
</code>
First we will load the scRNA-seq and the scATAC-seq data. We ma... | {
"filename": "analyses_3.SCENIC-V10-V2_1.ipynb",
"repository": "aertslab/scenicplus",
"query": "transformed_from_existing",
"size": 123784,
"sha": ""
} |
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