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node_metadata.tsv

A table with one row per unique genomic region (node) involved in a structural duplication.

Column	Description
id	Unique ID for the genomic region (chr:start-end)
gene_name	Ensembl or Liftoff-assigned gene name overlapping this region (if a

mr-eyes

function addOrUpdateAgendaBarWithProgressV5() {
  const pres   = SlidesApp.getActivePresentation();
  const slides = pres.getSlides();

  // 0) Clear any existing timeline shapes on ALL slides
  slides.forEach(slide => {
    slide.getPageElements().forEach(el => {
      const t = el.getTitle && el.getTitle();
      if (t && t.startsWith('AGENDA_')) {
        el.remove();

mr-eyes

name: kspider
channels:
  - conda-forge
  - bioconda
dependencies:
  - python=3.9
  - pip
  - sourmash
  - pip:
    - kSpider

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#include <Python.h>
#include <iostream>

using namespace std;

class toANI {
public:
    PyObject* moduleMainString, * moduleMain, * func;

    toANI() {

mr-eyes

import retworkx as rx
from tqdm import tqdm
import argparse

parser = argparse.ArgumentParser()

parser.add_argument('--csv', type=str, required=True, help="pairwise csv file")
parser.add_argument('--cutoff', type=int, required=True,
                    help="clustering threshold (0:100)")
parser.add_argument('--mode', type=str, required=True, choices=['ani', 'cont'],

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"""
Using the new NCBI's datasets API to get the nearest available reference genomes for a given taxon or organism name.

Input: TAX_ID or Name

Output:
    1- The nearest organism with available reference genomes
    2- Accessions of the reference genomes

Requirements:

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parent	metadata
p1	DB_A
p2	DB_B
p3	DB_C
p4	DB_A
p5	DB_A
p6	DB_A
p7	DB_B
p8	DB_C
p9	DB_C

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# Validate kProcessor 1 index

from itertools import groupby
import os
import kProcessor as kp
import hashlib



class IntegralHasher:

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"""
Input: Unitigs Fasta file generated from BCALM.
Output: CSV File with the following format

A. Column(1): Connected Component ID
B. Column(2:): Unitigs ID(s)

Run:
python unitigs_to_connected_components.py <unitigs_path>
"""

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