endolith

function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
%        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
%        maxima and minima ("peaks") in the vector V.
%        MAXTAB and MINTAB consists of two columns. Column 1
%        contains indices in V, and column 2 the found values.
%      
%        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
%        in MAXTAB and MINTAB are replaced with the corresponding
%        X-values.

feczo

Pre-requisite: go to http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html download http://the.earth.li/~sgtatham/putty/latest/x86/putty.exe and http://the.earth.li/~sgtatham/putty/latest/x86/puttygen.exe

Run puttygen and generate the key

1. Click 'Generate' move the mouse around as instructed and wait

danielecook

#!bin/python

import gzip
import io
import sys
import os

# This file will generate a bedfile of the masked regions a fasta file.

# STDIN or arguments

jlln

def splitDataFrameList(df,target_column,separator):
    ''' df = dataframe to split,
    target_column = the column containing the values to split
    separator = the symbol used to perform the split

    returns: a dataframe with each entry for the target column separated, with each element moved into a new row. 
    The values in the other columns are duplicated across the newly divided rows.
    '''
    def splitListToRows(row,row_accumulator,target_column,separator):
        split_row = row[target_column].split(separator)

mikheyev

ERCC-00002	ERCC	exon	1	1061	0.000000	+	.	gene_id "ERCC-00002"; transcript_id "DQ459430";
ERCC-00003	ERCC	exon	1	1023	0.000000	+	.	gene_id "ERCC-00003"; transcript_id "DQ516784";
ERCC-00004	ERCC	exon	1	523	0.000000	+	.	gene_id "ERCC-00004"; transcript_id "DQ516752";
ERCC-00009	ERCC	exon	1	984	0.000000	+	.	gene_id "ERCC-00009"; transcript_id "DQ668364";
ERCC-00012	ERCC	exon	1	994	0.000000	+	.	gene_id "ERCC-00012"; transcript_id "DQ883670";
ERCC-00013	ERCC	exon	1	808	0.000000	+	.	gene_id "ERCC-00013"; transcript_id "EF011062";
ERCC-00014	ERCC	exon	1	1957	0.000000	+	.	gene_id "ERCC-00014"; transcript_id "DQ875385";
ERCC-00016	ERCC	exon	1	844	0.000000	+	.	gene_id "ERCC-00016"; transcript_id "DQ883664";
ERCC-00017	ERCC	exon	1	1136	0.000000	+	.	gene_id "ERCC-00017"; transcript_id "DQ459420";
ERCC-00019	ERCC	exon	1	644	0.000000	+	.	gene_id "ERCC-00019"; transcript_id "DQ883651";

fadere

import csv
import requests
import bs4
import argparse

# parser = argparse.ArgumentParser(description='Process a list of search terms.')
# parser.add_argument('terms', metavar='N', type=str, nargs='+',
#                    help='comma separated list of terms to search for')

# args = parser.parse_args()

tjanez

#!/usr/bin/env perl
#
# Downloaded from http://www.uppmax.uu.se/system/files/fastqFormatDetect.pl_.txt on Jan 19, 2016.
#
# Author: Martin Dahlo / modified Jacques Dainat
#
# Usage:  perl scriptname.pl <infile> [-a -t <max seconds to search>]
# ex.
# perl scriptname.pl reads.fq
# perl scriptname.pl reads.fq -a

ckandoth

Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.

To follow these instructions, we'll assume you have these packaged essentials installed:

sudo yum install -y curl rsync tar make perl perl-core
## OR ##
sudo apt-get install -y curl rsync tar make perl perl-base

VEP requires Perl 5.10 or newer, but I'd strongly recommend at least 5.18. If your system Perl is outdated, then follow this gist to set up Perl 5.22. Once that's done, set temporary shell variables pointing to where Perl and its libraries live. Change this as needed for y

ckandoth

Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.

To follow these instructions, we'll assume you have these packaged essentials installed:

## For Debian/Ubuntu system admins ##
sudo apt-get install -y build-essential git libncurses-dev

## For RHEL/CentOS system admins ##
sudo yum groupinstall -y 'Development Tools'

sudo yum install -y git ncurses-devel

ckandoth

Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.

To follow these instructions, we'll assume you have these packaged essentials installed:

## For Debian/Ubuntu system admins ##
sudo apt-get install -y build-essential git libncurses-dev

## For RHEL/CentOS system admins ##
sudo yum groupinstall -y 'Development Tools'
sudo yum install -y git ncurses-devel