ReAlign-N: an integrated realignment approach for multiple nucleic acid sequence alignment, combining global and local realignments

ReAlign-N is a tool written in C++11 for realigning the multiple nucleic acid sequence alignment. It runs on Linux.

🔨Installation and Usage

1.1 Linux/WSL(Windows Subsystem for Linux ) - from Anaconda

1.Install WSL for Windows. Instructional video 1 or 2 (Copyright belongs to the original work).

2.Download and install Anaconda. Download Anaconda for different systems here. Instructional video of anaconda installation 1 or 2 (Copyright belongs to the original work).

3.Install ReAlign-N.

#1 Create and activate a conda environment for ReAlign-N
conda create -n realign_n_env
conda activate realign_n_env

#2 Add channels to conda
conda config --add channels malab

#3 Install ReAlign-N
conda install -c malab realign_n

#4 Test ReAlign-N
realign_n -h

1.2 Linux/WSL(Windows Subsystem for Linux ) - from the source code

1.Download and Compile the source code. (Make sure your version of gcc >= 9.4.0)

#1 Download
git clone https://github.com/malabz/ReAlign-N.git

#2 Open the folder
cd ReAlign-N

#3 Compile
make

#4 Test ReAlign-N
./realign_n -h

2 Usage

Usage: /.realign_n [-r] path [-a] path [-o] path [-m] distance [-e] distance [-p] pattern [-h]

  Necessary arguments:
    -r  Specify the path of raw data, a file in FASTA format.
    -a  Specify the path of initial alignment, a file in FASTA format.

  Optional arguments:
    -o  Specify the output for ReAlign-N, a file in FASTA format.
    -m  Specify the minium split distance of match (default based on the similarity).
    -e  Specify the minium split distance of entropy (default based on the similarity).
    -p  Specify the pattern of ReAlign-N (default pattern: 1).
          1 for local realignment followed by global realignment.
          2 for global realignment followed by local realignment.
    -h  Print the help message.

3 ReAlign-N Parameters Description

1. Usage:

/.realign_n [-r] path [-a] path [-o] path [-m] distance [-e] distance [-p] pattern [-h]

2. Required Arguments:

• -r

  Specify the path to the raw data file in FASTA format.

  Example: -r /path/to/raw_data.fasta

• -a

  Specify the path to the initial alignment file in FASTA format.

  Example: -a /path/to/initial_alignment.fasta

3. Optional Arguments:

• -o

  Specify the path for the ReAlign-N output file in FASTA format.

  If not provided, the output will be saved to the default path（/CURRENT_PATH/realign_n_result.fas）.

  Example: -o /path/to/output.fasta

• -m

  Specify the minimum split distance for matching.

  If not provided, the default value is automatically determined based on the similarity of the input data.

  Example: -m 10 sets the minimum split distance for matches to 10.

• -e

  Specify the minimum split distance for entropy.

  If not provided, the default value is automatically determined based on the similarity of the input data.

  Example: -e 5 sets the minimum split distance for entropy to 5.

• -p

  Specify the alignment pattern for ReAlign-N (default pattern: 1).

  1: Local realignment followed by global realignment.

  2: Global realignment followed by local realignment.

  Example: -p 2 sets the pattern to global realignment followed by local realignment.

• -h

  Print the help message displaying all available parameters and their descriptions.

4. Minimum split distances correspond to different average similarity levels and sequence counts.

• The default value for match

  Avg similarity	Sequences Counts	Distance
  [0.99,1]	<200	5
  [0.96,0.99)	<200	10
  [0.93,0.96)	<200	15
  [0.90,0.93)	<200	20
  [0.80,0.90)	<200	30
  [0,0.80)	<200	50
  [0.90,1]	>=200	10
  [0,0.90)	>=200	50

• The default value for entropy

  Avg similarity	Sequences Counts	Distance
  [0.97,1]	<200	5
  [0.90,0.97)	<200	50
  [0,0.90)	<200	5
  [0.90,1]	>=200	10
  [0,0.90)	>=200	50

• Note

  If the user does not specify -e AND -m in the command line, their default values are set based on the similarity observed during the initial alignment.

  If the user specifies only -e OR -m, the program will use the provided parameter for calculation, while the unspecified parameter will be determined based on the default value (i.e., the similarity of the initial alignment).

  If both -e AND -m are specified, the program will calculate entirely based on the user’s input.

🔬Test dataset and the use case

1. Information about the test dataset

Dataset	Sequences Num	Repeats Num	Avg Length	Similarity
16S rRNA	1000	10	about 1440bp	The average similarity is about 72%
mt genome	200	10	about 16570bp	The average similarity is about 99%
23S rRNA	500	10	about 3120bp	The average similarity is about 92%
16S-like rRNA	100	9	about 1550bp	14 sets of data with different similarities (99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 85%, 80%, 75%, 70%)
mt-like genome	100	9	about 16000bp	14 sets of data with different similarities (99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 85%, 80%, 75%, 70%)
SARS-CoV-2-like genome	100	9	about 29000bp	14 sets of data with different similarities (99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 85%, 80%, 75%, 70%)
CIPRES-128	255	9	about 1550bp	The average similarity is about 80%
CIPRES-256	511	9	about 1550bp	The average similarity is about 80%
CIPRES-512	1023	9	about 1550bp	The average similarity is about 80%
CIPRES-1024	2047	9	about 1550bp	The average similarity is about 80%

2. The use case

# Download data
wget http://lab.malab.cn/soft/ReAlign-N/data/16s_like.tar.gz

# Unzip data
tar -zxvf 16s_like.tar.gz

# Get the folder path
cd 16s_like

# Run ReAlign-N
./realign_n -r raw_data/16s_similarity_70_1.fas -a msa_results/16s_similarity_70_1_clustalo.fas -o 16s_similarity_70_1_clustalo_realign_n.fas -p 1

📍Reminder

1. Currently ReAlign-N is ONLY available for DNA/RNA.
2. Please ensure that the sequence contains only AGCT (for DNA) or AGCU (for RNA).
3. Please ensure that the sequence ID entered into ReAlign is unique.
4. MAFFT installation is required for the utilization of ReAlign-N.

🖥️Environment

System	GCC version
Linux	GCC 9.4.0
WSL	GCC 9.4.0

🔖Citation

👋Contacts

The software tools are developed and maintained by 🧑‍🏫ZOU's lab.

If you find any bug, welcome to contact us on the issues page or email us at 👉📩.

More tools and infomation can visit our github.