BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA, and protein sequences at sequence level and residue level based on machine learning approaches

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DNA-Analysis2.0

RNA-Analysis2.0

Protein-Analysis2.0

RNA_Residue

Mode(?)	---------------------------- ** Residue-Level ** ---------------------------- One-hot Position-specific-2 Position-specific-3 Position-specific-4 DPC SS ---------------------------- ** Sequence-Level ** ---------------------------- NAC DNC TNC CKSNAP NCP ANF Zcurve Kmer Mismatch Subsequence DAC DCC DACC MAC GAC NMBAC PC-PseDNC-General SC-PseDNC-General Triplet PseSSC PseDPC (?)
Feature selection(?)	mutual information chi-square
Dimensions(?)	2000 1000 500 400 300 200 100 50
Fragment(?)	No Yes
Window size(?)
Type of problem(?)	Binary classification Multiclass classification
Number of labels(?)	2 3 4 5 6
Machine learning algorithm(?)	Support Vector Machine Random Forest Conditional Random Field
Parameter optimization(?)	No Yes
C(cost)(?)	2⁻¹ 2⁰ 2¹ 2² 2³ 2⁴ 2⁵ 2⁶ 2⁷ From: 2⁻¹ 2⁰ 2¹ To: 2⁵ 2⁷
g(gamma)(?)	2⁻⁷ 2⁻⁶ 2⁻⁵ 2⁻⁴ 2⁻³ 2⁻² 2⁻¹ 2⁰ 2¹ 2² 2³ From: 2⁻⁷ 2⁻⁵ 2⁻³ To: 2⁻¹ 2⁰ 2³
n_estimators(?)	50 100 200 300 400 500 From: 100 200 300 To: 400 500 600
neighbors(?)	3 5 7 9 11 13 From: 3 5 7 To: 11 13 15
Performance measure for parameter optimization(?)	ACC MCC AUC
Sampling(?)	None Under sampling
Cross validation(?)	5-fold cross validation Independent dataset test
Input the independent dataset in FASTA format (?): (minimum 5 sequences for each submission) or upload a file in FASTA format (?): Labels of independent dataset (?):
Input your e-mail(optional)(?)
Sequence Input:Input RNA sequences in FASTA format(?): ((5-100 sequences for each submission) or upload a file in FASTA format(?):
Label Input:Input RNA sequences label (training set) in FASTA format (?): (each RNA residue must be aligned with the label) or upload a file in FASTA format (?):

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