The file description below is gathered from: http://genome.ucsc.edu/FAQ/FAQformat#format1.

BED6: A BED file where each feature is described by chrom, start, end, name, score, and strand. For example: chr1 11873 14409 uc001aaa.3 0 +

1. chrom:- The name of the chromosome on which the genome feature exists. Any string can be used.

2. start:- The zero-based starting position of the feature in the chromosome. The first base in a chromosome is numbered 0. The start position in each BED feature is therefore interpreted to be 1 greater than the start position listed in the feature. For example, start=9, end=20 is interpreted to span bases 10 through 20,inclusive.

3. end:- The one-based ending position of the feature in the chromosome. The end position in each BED feature is one-based.

4. name:- Defines the name of the BED feature. Any string can be used. For example, "LINE", "Exon3", "HWIEAS_0001:3:1:0:266#0/1", or "my_Feature".

5. score:- The UCSC definition requires that a BED score range from 0 to 1000, inclusive. However, bedtools allows any string to be stored in this field in order to allow greater flexibility in annotation features. For example, strings allow scientific notation for p-values, mean enrichment values, etc. It should be noted that this flexibility could prevent such annotations from being correctly displayed on the UCSC browser. Any string can be used. For example, 7.31E-05 (p-value), 0.33456 (mean enrichment value), "up", "down", etc.

6. strand:- Defines the strand - either '+' or '-'.

Peak data were refined based on the length distribution and peaks laying in extreme range (length >300bases & <5bases) for model building.

1. The train-test split is a technique for evaluating the performance of a machine learning algorithm.

2. The procedure involves taking a dataset and dividing it into two subsets. The first subset is used to fit the model and is referred to as the training dataset. The second subset is not used to train the model; instead, the input element of the dataset is provided to the model, then predictions are made and compared to the expected values. This second dataset is referred to as the test dataset.

a). Train Dataset: Used to fit the machine learning model.

b). Test Dataset: Used to evaluate the fit machine learning model.

3. The objective is to estimate the performance of the machine learning model on new data: data not used to train the model.