3DFI | 3DFI pipeline predicts the 3D structure | Genomics library
kandi X-RAY | 3DFI Summary
kandi X-RAY | 3DFI Summary
Inferring the function of proteins using computational approaches usually involves performing some sort of homology search based on sequences or structures. In sequence-based searches, nucleotide or amino acid sequences are queried against known proteins or motifs using tools such as BLAST, DIAMOND, HHBLITS or HMMER, but those searches may fail if the proteins investigated are highly divergent. In structure-based searches, proteins are searched instead at the 3D level for structural homologs. Because structure often confers function in biology, structural homologs often share similar functions, even if the building blocks are not the same (i.e. a wheel made of wood or steel is still a wheel regardless of its composition). Using this approach, we might be able to infer putative functions for proteins that share little to no similarity at the sequence level with known proteins, assuming that a structural match can be found. To perform structure-based predictions we need 3D structures — either determined experimentally or predicted computationally — that we can query against other structures, such as those from the RCSB PDB. We also need tools that can search for homology at the structural level. Several tools are now available to predict protein structures, many of which are implemented as web servers for ease of use. A listing can be found at CAMEO, a website that evaluates their accuracy and reliability. Fewer tools are available to perform searches at the 3D levels (e.g. SSM and GESAMT). SSM is implemented in PDBeFold while GESAMT is part of the CCP4 package. Although predicting the structure of a protein and searching for structural homologs can be done online, for example by using SWISS-MODEL and PDBeFold, genomes often code for thousands of proteins and applying this approach on a genome scale using web portals would be time consuming and error prone. We implemented the 3DFI pipeline to enable the use of structure-based homology searches at a genome-wide level from the command line.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of 3DFI
3DFI Key Features
3DFI Examples and Code Snippets
Lines of Code : 265License : Permissive (MIT)
Copy## Creating a working directory for 3DFI:
export RESULTS=~/Results_3DFI
export FSAOUT=$RESULTS/FASTA
mkdir -p $RESULTS
## Running split_Fasta.pl on provided examples:
split_Fasta.pl \
-f $TDFI_HOME/Examples/FASTA/*.fasta \
-o $FSAOUT
## Creat sudo dnf install aria2
LICENSE=XXXXX ## replace XXXXX by modeller license
MODELLER=modeller-10.1-1.x86_64.rpm
sudo env KEY_MODELLER=$LICENSE rpm -Uvh $MODELLER
export CCP4=/opt/xtal/CCP4/ccp4-7.1/bin/
export PATH=$PATH:$CCP4
sudo dnf install ucsf- split_PDB.pl \
-p files.pdb \
-o output_folder \
-e pdb
rename_files.pl \
-o 'i{0,1}-t26_1-p1' \
-n '' \
-f *.fasta
## The files can eat through GPU VRAM very quickly. 8 Gb is usually insufficient.
conda create -n tfgpu python=3.7 Community Discussions
Trending Discussions on Genomics
QUESTION
I´m working with two text files that look like this: File 1
...ANSWER
Answered 2022-Apr-09 at 00:49Perhaps you are after this?
QUESTION
I'm using the software plink2 (https://www.cog-genomics.org/plink/2.0/) and I'm trying to iterate over 3 variables.
This software admits an input file with .ped extention file and an exclude file with .txt extention which contains a list of names to be excluded from the input file.
The idea is to iterate over the input files and then over exclude files to generate single outputfiles.
- Input files: Highland.ped - Midland.ped - Lowland.ped
- Exclude-map files: HighlandMidland.txt - HighlandLowland.txt - MidlandLowland.txt
- Output files: HighlandMidland - HighlandLowland - MidlandHighland - MidlandLowland - LowlandHighland - LowlandMidland
The general code is:
...ANSWER
Answered 2021-Dec-09 at 23:50Honestly, I think your current code is quite clear; but if you really want to write this as a loop, here's one possibility:
QUESTION
From this example string:
...ANSWER
Answered 2021-Dec-09 at 01:11use regexp_extract(col, r"&q;Stockcode&q;:([^/$]*?),&q;.*")
if applied to sample data in your question - output is
QUESTION
I am making a code which takes in jumble word and returns a unjumbled word , the data.json contains a list and here take a word one-by-one and check if it contains all the characters of the word and later checking if the length is same , but the problem is when i enter a word as helol then the l is checked twice and giving me some other outputs including the main one(hello). i know why does it happen but i cant get a fix to it
...ANSWER
Answered 2021-Nov-25 at 18:33As I understand it you are trying to identify all possible matches for the jumbled string in your list. You could sort the letters in the jumbled word and match the resulting list against sorted lists of the words in your data file.
QUESTION
I am trying to use plink1.9 to split multiallelic into biallelic. The input is that
...ANSWER
Answered 2021-Nov-17 at 09:45I used bcftools to complete the task.
QUESTION
I have a FASTA file that has about 300000 sequences but some of the sequences are like these
...ANSWER
Answered 2021-Oct-12 at 20:28You can match your non-X containing FASTA entries with the regex >.+\n[^X]+\n. This checks for a substring starting with > having a first line of anything (the FASTA header), which is followed by characters not containing an X until you reach a line break.
For example:
QUESTION
For example, I have two strings:
...ANSWER
Answered 2021-Oct-04 at 22:27For your example your pattern would be:
QUESTION
I am currently trying to run genomic analyses pipelines using Hail(library for genomics analyses written in python and Scala). Recently, Apache Spark 3 was released and it supported GPU usage.
I tried spark-rapids library start an on-premise slurm cluster with gpu nodes. I was able to initialise the cluster. However, when I tried running hail tasks, the executors keep getting killed.
On querying in Hail forum, I got the response that
That’s a GPU code generator for Spark-SQL, and Hail doesn’t use any Spark-SQL interfaces, only the RDD interfaces.
So, does Spark3 not support GPU usage for RDD interfaces?
...ANSWER
Answered 2021-Sep-23 at 05:53As of now, spark-rapids doesn't support GPU usage for RDD interfaces.
Source: Link
Apache Spark 3.0+ lets users provide a plugin that can replace the backend for SQL and DataFrame operations. This requires no API changes from the user. The plugin will replace SQL operations it supports with GPU accelerated versions. If an operation is not supported it will fall back to using the Spark CPU version. Note that the plugin cannot accelerate operations that manipulate RDDs directly.
Here, an answer from spark-rapids team
Source: Link
We do not support running the RDD API on GPUs at this time. We only support the SQL/Dataframe API, and even then only a subset of the operators. This is because we are translating individual Catalyst operators into GPU enabled equivalent operators. I would love to be able to support the RDD API, but that would require us to be able to take arbitrary java, scala, and python code and run it on the GPU. We are investigating ways to try to accomplish some of this, but right now it is very difficult to do. That is especially true for libraries like Hail, which use python as an API, but the data analysis is done in C/C++.
QUESTION
I have 1500 files with the same format (the .scount file format from PLINK2 https://www.cog-genomics.org/plink/2.0/formats#scount), an example is below:
...ANSWER
Answered 2021-Sep-07 at 11:10a tidyverse solution
QUESTION
I have been implementing a suite of RecordBatchReaders for a genomics toolset. The standard unit of work is a RecordBatch. I ended up implementing a lot of my own compression and IO tools instead of using the existing utilities in the arrow cpp platform because I was confused about them. Are there any clear examples of using the existing compression and file IO utilities to simply get a file stream that inflates standard zlib data? Also, an object diagram for the cpp platform would be helpful in ramping up.
...ANSWER
Answered 2021-Jun-02 at 18:58Here is an example program that inflates a compressed zlib file and reads it as CSV.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install 3DFI
A CUDA-enabled NVIDIA GPU (>= 24 Gb VRAM; >= 6.1 compute capability)
A fast 4 Tb+ SSD
At least 64 Gb of RAM
The deep-learning based protein structure predictors AlphaFold2 and RoseTTAFold leverage the NVIDIA CUDA framework to accelerate computations on existing GPUs. Although small proteins might fit within 8Gb of video RAM (VRAM), larger proteins will require more VRAM (the RoseTTAFold authors used a 24 Gb VRAM GPU in their paper). Both AlphaFold2 and RoseTTAFold can run without GPU acceleration, but doing so is much slower and is only recommended for small numbers of proteins. The template-based protein structure predictor RaptorX does not require any GPU.
Both AlphaFold2 and RoseTTAFold leverage hhblits from HH-suite3 to perform hidden Markov model searches as part of their protein structure prediction processes. These searches are I/O intensive and can be greatly sped up by putting the databases to query onto an NVME SSD. Because the Alphafold databases are over 2.2 Tb in size once uncompressed, a fast SSD of at least 4TB is recommended to store all databases in a single location. Running hhblits on hard drives is possible (if slower), but we have seen hhblits searches crash on a few occasions when an hard drive's I/O was being saturated.
Using AlphaFold2 with its --full_dbs preset can require a large amount of system memory. The AlphaFold --reduced_dbs preset uses a smaller memory footprint. 3DFI was tested on machines with a minimum of 64 Gb of RAM but may work on machines with more modest specifications.
If investigating large datasets, a 10 Tb+ storage solution is recommended (hard drives are fine) to store the results. AlphaFold often outputs over 50 Gb of data per protein.
The lightweight aria2 download utility tool.
At least one of the following protein structure prediction tools: A customized version of AlphaFold2 (Deep-learning-based) Requires Docker RoseTTAFold (Deep-learning-based) Requires Conda, PyRosetta (Python-3.7 Release) RaptorX (Template-based) Requires MODELLER
A structural homology search tool: GESAMT via CCP4
An alignment/visualization tool: ChimeraX 1.2.5
Perl5 and the additonal scripting module: PerlIO::gzip
When run, the 3DFI pipeline will search for the following environment variables ($ALPHAFOLD_HOME, $ROSETTAFOLD_HOME and/or $RAPTORX_HOME) depending on the requested protein structure predictor(s).
The 3DFI installation and database directories will be set as environment variables ($TDFI_HOME and $TDFI_DB, respectively).
Prepare FASTA files (single or multifasta) for protein folding
Run the selected protein structure predictor(s)
Perform structural homology searches between predicted structures and RCSB PDB proteins
Align the predicted proteins with their structural homologs for later visualization with ChimeraX
Because the protein structure prediction step is time-consuming even with GPU acceleration, we recommend running only one predictor at a time if using large protein datasets. The average AlphaFold folding time on our AMD Ryzen 5950X/NVIDIA RTX A6000 workstation was 31.59 minutes per protein (~ 50 proteins/day) on a ~1,900 protein dataset, with computation times as low and high as 9.07 and 4282.32 minutes per protein, respectively.
If interrupted, the pipeline can be resumed by re-entering the same command line. Previously computed protein structures, structural matches and alignments will be skipped.
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