HiTE - 3.1.2

solve bugs in generating TE library

HiTE - 3.1.1

Classifying a large LTR library during the maize processing involves the following steps:

1. Utilizing a non-redundant LTR library to retrieve full-length LTRs, subsequently classified by NeuralTE.
2. Assigning labels derived from the classified LTRs to the non-redundant LTR library.
3. Employing the NeuralTE_model.h5 model from NeuralTE to classify LTRs.
4. Allowing users to specify the type of classification labels (Wicker or RepeatMasker system) using the --is_wicker parameter.

HiTE - 3.1.0

1. Use NeuralTE for TE classification instead of the previous RepeatClassifier.
2. No need to configure additional Dfam libraries for TE classification.
3. Individually classifying full-length LTRs and subsequently merging them into the final TE library.

HiTE - 3.0.4

1. modify BM_HiTE (RepeatMasker -div 40 to 5).

HiTE - 3.0.3

split Helitron input as 50K each file

HiTE - 3.0.2

1. fix bugs in nextflow
2. add BM_HiTE function
3. fix bugs in EAHelitron identification

HiTE - 3.0.1

1. update docker file (download code from zenodo)

HiTE - 3.0.0

1. add the mask mode
2. add non-LTR de novo mode
3. remove redundant code
4. update README

HiTE - 2.0.6

1. fix bugs in experiment reproduction.
2. fix bugs in nextflow.

HiTE - 2.0.5

1. Add function "annotate genome" with parameter --annotate 1