Our research

Workflow of scSLAM-seq and GRAND-SLAM.
4sU incorporated into new RNA is chemically converted into a cytosine analog after cell lysis or in fixed cells. The resulting mismatches from scRNA-seq enable to estimate the new to total RNA (NTR) ratio per gene/cell by using statistical modeling. Distinguishing old and new RNA enables gene regulatory network inference, functional genomics approaches and to directly analyze transcriptional bursting.

Ribosome profiling (or Ribo-seq) is a technique to identify and quantify translation of ORFs with subcodon resolution.

It is based on sequencing the ribosome footprints, which are RNA fragments protected by the ribosome from enzymatic digestion during preparation of samples. Due to stringent RNase conditions used and the matter of the fact that ribosomes translocate from codon to codon, the positions of sequencing reads show a characteristic periodicity with respect to the frame of translation. Also, for many reads the P site codon for the corresponding ribosome is at position 12 within the read. 

This procedure of mapping reads to P site codons, however, is too inaccurate to properly resolve many ORFs, in particular most of the short upstream ORFs (uORFs). We have developed Probabilistic inference of codon activities by an EM algorithm (PRICE (external link, opens in a new window)), a new algorithm to estimate P site codon positions with drastically improved signal to noise ratio. Moreover, a new statistical test included in PRICE enables us to reliable resolve complex cases such as arising with multiple overlapping ORFs. 

Validation of newly identified ORFs has been an unsolved issue so far. Peptides derived from short ORFs often escape detection by mass spectrometry experiments. We reasoned that they should be well represented in MHC-I complexes, as peptide presentation via this pathway is believed to be dependent on translation rates and not on protein abundance. Indeed, we found hundreds of cryptic peptides derived from short ORFs in MHC-I ligandome experiments.

Left: Comparison of approaches for mapping reads to codons with respect to signal (total number of reads mapped in-frame) and signal-to-noise ratio (noise: reads mapped out-of-frame to annotated ORFs). Color-coded according to the key to indicate deterministic mapping of read classes defined by length and 5′ mismatch state and of combinations of read classes (basic, ignoring 5′ mismatches; extended, considering 5′ mismatches; top 4, combining the best read classes), and probabilistic mapping by PRICE. 
Right: Total amount of peptides detected in proteome and MHC I peptidome mass spectrometry experiments. The 1% peptide-identification FDR is indicated by a dashed line. Gray bars represent the peptides from ORFs also identified by ORF-RATER or Rp-Bp (for PRICE) or ORFs also identified by PRICE (for ORF-RATER and Rp-Bp).

Many problems in biology can only be solved by utilizing and combining more than one data set from large-scale experiments.

Herpesviral genomes are relatively large (e.g. ca. 150kb for HSV-1) and are known to encode many proteins (e.g. 80 known proteins for HSV-1). Since genome sequences became available, identification of genes and proteins was based on the prediction of open reading frames (ORFs), which were then extensively validated and characterized in the 1990s. However, modern high-throughput techniques now enable a more unbiased approach to comprehensively and accurately identify genetic elements in such small genomes. By using a large array of different data sets, we were able to extend the previous annotation of HSV-1 (external link, opens in a new window) to a total of 201 mRNAs and 284 ORFs. There were two very important lessons to learn: First, a discovery from a single large-scale data set might just represent an experimental artifact. The key for being accurate is to integrate more than one experimental technique. And second, to really understand translation and which proteins are made, the knowledge of the mRNAs (and transcription start sites) is essential.

Overview of the applied Omics approaches to re-annotate HSV-1. Viral gene expression was analyzed in primary human fibroblasts (HFF). The total RNA-seq, 4sU-seq and ribosome profiling data were recently published. To comprehensively identify transcription start site (TiSS), we performed cRNA-seq and dRNA-seq  as well as RNA-seq on subcellular RNA fractions from mock, wild-type and dICP27 infected cells. Furthermore, we reanalyzed recently published PacBio and MinION sequencing data. Translation start site (TaSS) profiling was performed by ribosome profiling following treatment of cells for 30 min with either Harringtonine or Lactimidomycin. Proteome analysis included two whole proteome data sets using SILAC and label-free mass spectrometry. The available time points and conditions are indicated by stars.

Products of short open reading frames are often rapidly degraded after translation. Therefore, they have the chance to enter the MHC-I peptide presentation pathway (see above). To be able to screen the large number of immunipeptidomics data sets available, without the need to build sequence databases based on Ribo-seq data, we developed the computational approach Proteogenomic Identification using Stratified Mixture models (Peptide-PRISM). Peptide-PRISM identified thousands of cryptic peptides and showed that cryptic peptides indeed contribute up to 15% of the MHC-I ligands in different tumor samples.

Peptide-PRISM identifies cryptic peptides in a melanoma sample (A) Workflow of Peptide-PRISM. (B) Number of peptides identified for sample MM15. Andromeda represents the originally published numbers, classic FDR and Peptide-PRISM are described in the methods (C) Novel and cryptic peptides consist of the same percentage of netMHCpan 4.0 predicted HLA-I binders as the published peptide set. Novel peptides are proteome-derived peptides identified by our approach but not in the original report. Error bars represent 95% binomial confidence intervals.