Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user's manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.

Motivation: In recent years, Transposable Elements (TEs) have been related to gene regulation. However, estimating the origin of expression of TEs through RNA-seq is complicated by multi-mapping reads coming from their repetitive sequences. Current approaches that address multi-mapping reads are focused in expression quantification and not in finding the origin of expression. Addressing the genomic origin of expressed TEs could further aid in understanding the role that TEs might have in the cell. Results: We have developed a new pipeline called TEcandidates, based on de novo transcriptome assembly to assess the instances of TEs being expressed, along with their location, to include in downstream DE analysis. TEcandidates takes as input the RNA-seq data, the genome sequence and the TE annotation file and returns a list of coordinates of candidate TEs being expressed, the TEs that have been removed and the genome sequence with removed TEs as masked. This masked genome is suited to include TEs in downstream expression analysis, as the ambiguity of reads coming from TEs is significantly reduced in the mapping step of the analysis. Availability and implementation: The script which runs the pipeline can be downloaded at http://www.mobilomics.org/tecandidates/downloads or http://github.com/TEcandidates/TEcandidates. Supplementary information: Supplementary data are available at Bioinformatics online.