Ruhshan

BLASTN 2.7.1+


Reference: Zheng Zhang, Scott Schwartz, Lukas Wagner, and Webb
Miller (2000), "A greedy algorithm for aligning DNA sequences", J
Comput Biol 2000; 7(1-2):203-14.



Database: cad_res_partial

Ruhshan

from django.conf import settings
from demoapp import models

def RequestExposerMiddleware(get_response):
    def middleware(request):
        models.exposed_request = request
        response = get_response(request)
        return response

return middleware

Ruhshan

cycler==0.10.0
Django==2.0.1
django-cors-headers==2.1.0
django-extensions==1.9.9
imutils==0.4.5
matplotlib==2.1.2
numpy==1.13.3
opencv-python==3.3.1.11
pandas==0.22.0
Pillow==5.0.0

Ruhshan

library("seqinr")
getncbiseq <- function(accession)
{
  require("seqinr") # this function requires the SeqinR R package
  # first find which ACNUC database the accession is stored in:
  dbs <- c("genbank","refseq","refseqViruses","bacterial")
  numdbs <- length(dbs)
  for (i in 1:numdbs)
  {
    db <- dbs[i]

Ruhshan

findcommunities <- function(mygraph,minsize)
  {
     # Function to find network communities in a graph
     # Load up the igraph library:
     require("igraph")
     # Set the counter for the number of communities:
     cnt <- 0
     # First find the connected components in the graph:
     myconnectedcomponents <- connectedComp(mygraph)
     # For each connected component, find the communities within that connected component:

Ruhshan

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	for epoch in range(n_epochs):
		train_data_batch_gen = get_train_data_batch(batch_size)
		epoch_loss = 0
		i=0
		while i < total_lines:
			train_x, train_y = next(train_data_batch_gen)
			
			batch_x = np.array(train_x)

Ruhshan

def get_train_data_batch(n):
    features = []
    with open('data.csv') as f:
        count=0
        for line in f:
            splitted = line.split(';')
            featureset = [int(float(x)) for x in splitted[:len(splitted)-1]]
            label = int(float(splitted[-1].rstrip()))
            count+=1
            if count % n==0:

Ruhshan

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())

	for epoch in range(n_epochs):
		epoch_loss = 0
		i=0
		while i < len(train_x):
			start = i
			end = i+batch_size
			batch_x = np.array(train_x[start:end])

Ruhshan

library(Peptides)
library(seqinr)

get_comp <- function(fasta){
  s=getSequence(fasta, as.string = TRUE);
  sname=getName(fasta);
  
  comp = aaComp(s);
  
  r<-c(name=sname,

Ruhshan

{"test_name":"TCHO","level_1_lower_range":10,"level_1_upper_range":20,"level_2_lower_range":20,"level_2_upper_range":30,"level_3_lower_range":30,"level_3_upper_range":40}