Background: Recently high-throughput sequencing (HTS) using next generation sequencing techniques became useful in digital gene expression profiling. Our study introduces analysis options for HTS data based on mapping to miRBase or counting and grouping of identical sequence reads. Those approaches allow a hypothesis free detection of miRNA differential expression. Methods: We compare our results to microarray and qPCR data from one set of RNA samples. We use Illumina platforms for microarray analysis and miRNA sequencing of 20 samples from benign follicular thyroid adenoma and malignant follicular thyroid carcinoma. Furthermore, we use three strategies for HTS data analysis to evaluate miRNA biomarkers for malignant versus benign follicular thyroid tumors. Results: High correlation of qPCR and HTS data was observed for the proposed analysis methods. However, qPCR is limited in the differential detection of miRNA isoforms. Moreover, we illustrate a much broader dynamic range of HTS compared to microarrays for small RNA studies. Finally, our data confirm hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p and both hsa-miR-144-3p and hsa-miR-144-5p as potential follicular thyroid cancer biomarkers. Conclusions: Compared to microarrays HTS provides a global profile of miRNA expression with higher specificity and in more detail. Summarizing of HTS reads as isoform groups (analysis pipeline B) or according to functional criteria (seed analysis pipeline C), which better correlates to results of qPCR are promising new options for HTS analysis. Finally, data opens future miRNA research perspectives for HTS and indicates that qPCR might be limited in validating HTS data in detail.

Background: Recently high-throughput sequencing (HTS) using next generation sequencing techniques became useful in digital gene expression profiling. Our study introduces analysis options for HTS data based on mapping to miRBase or counting and grouping of identical sequence reads. Those approaches allow a hypothesis free detection of miRNA differential expression. Methods: We compare our results to microarray and qPCR data from one set of RNA samples. We use Illumina platforms for microarray analysis and miRNA sequencing of 20 samples from benign follicular thyroid adenoma and malignant follicular thyroid carcinoma. Furthermore, we use three strategies for HTS data analysis to evaluate miRNA biomarkers for malignant versus benign follicular thyroid tumors. Results: High correlation of qPCR and HTS data was observed for the proposed analysis methods. However, qPCR is limited in the differential detection of miRNA isoforms. Moreover, we illustrate a much broader dynamic range of HTS compared to microarrays for small RNA studies. Finally, our data confirm hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p and both hsa-miR-144-3p and hsa-miR-144-5p as potential follicular thyroid cancer biomarkers. Conclusions: Compared to microarrays HTS provides a global profile of miRNA expression with higher specificity and in more detail. Summarizing of HTS reads as isoform groups (analysis pipeline B) or according to functional criteria (seed analysis pipeline C), which better correlates to results of qPCR are promising new options for HTS analysis. Finally, data opens future miRNA research perspectives for HTS and indicates that qPCR might be limited in validating HTS data in detail.:Background; Methods; Results; Discussion; Conclusions

Der Ribonukleinsäure (RNS) Stoffwechsel umfasst verschiedene Schritte, beginnend mit der Transkription der RNS über die Translation bis zum RNA Abbau. Poly(A) Schwänze befinden sich am Ende der meisten der Boten-RNS, schützen die RNA vor Abbau und stimulieren Translation. Die Deadenylierung von Poly(A) Schwänzen limitiert den Abbau von RNS. Bisher wurde RNS Abbau meist im Kontext von cytoplasmatischen Prozessen untersucht, ob und wie RNS Deadenylierung und Abbau in Nukleus erfolgen ist bisher unklar. Es wurde daher eine neue Methode zur genomweiten Bestimmung von Poly(A) Schwanzlänge entwickelt, welche FLAM-Seq genannt wurde. FLAM-Seq wurde verwendet um Zelllinien, Organoide und C. elegans RNS zu analysieren und es wurde eine signifikante Korrelation zwischen 3’-UTR und Poly(A) Länge gefunden, sowie für viele Gene ein Zusammenhang von alternativen 3‘-UTR Isoformen und Poly(A) Länge. Die Untersuchung von Poly(A) Schwänzen von nicht-gespleißten RNS Molekülen zeige, dass deren Poly(A) Schwänze eine Länge von mehr als 200 nt hatten. Die Analyse wurde durch eine Inhibition des Spleiß-Prozesses validiert. Die Verwendung von Methoden zur Markierung von RNS, welche die zeitliche Auflösung der RNS Prozessierung ermöglicht, deutete auf eine Deadenylierung der Poly(A) Schwänze schon wenige Minuten nach deren Synthesis hin. Die Analyse von subzellulären Fraktionen zeigte, dass diese initiale Deadenylierung ein Prozess im Nukleus ist. Dieser Prozess ist gen-spezifisch und Poly(A) Schwänze von bestimmten Typen von Transkripten, wie nuklearen langen nicht-kodierende RNS Molekülen waren nicht deadenyliert. Um Enzyme zu identifizieren, welche die Deadenylierung im Zellkern katalysieren, wurden verschiedene Methoden wie RNS-abbauende Cas Systeme, siRNAs oder shRNA Zelllinien verwendet. Trotz einer effizienten Reduktion der RNS Expression entsprechender Enzymkomplexe konnten keine molekularen Phänotypen identifiziert werden welche die Poly(A) Länge im Zellkern beeinflussen. / The RNA metabolism involves different steps from transcription to translation and decay of messenger RNAs (mRNAs). Most mRNAs have a poly(A) tail attached to their 3’-end, which protects them from degradation and stimulates translation. Removal of the poly(A) tail is the rate-limiting step in RNA decay controlling stability and translation. It is yet unclear if and to what extent RNA deadenylation occurs in the mammalian nucleus. A novel method for genome-wide determination of poly(A) tail length, termed FLAM-Seq, was developed to overcome current challenges in sequencing mRNAs, enabling genome-wide analysis of complete RNAs, including their poly(A) tail sequence. FLAM-Seq analysis of different model systems uncovered a strong correlation between poly(A) tail and 3’-UTR length or alternative polyadenylation. Cytosine nucleotides were further significantly enriched in poly(A) tails. Analyzing poly(A) tails of unspliced RNAs from FLAM-Seq data revealed the genome-wide synthesis of poly(A) tails with a length of more than 200 nt. This could be validated by splicing inhibition experiments which uncovered potential links between the completion of splicing and poly(A) tail shortening. Measuring RNA deadenylation kinetics using metabolic labeling experiments hinted at a rapid shortening of tails within minutes. The analysis of subcellular fractions obtained from HeLa cells and a mouse brain showed that initial deadenylation is a nuclear process. Nuclear deadenylation is gene specific and poly(A) tails of lncRNAs retained in the nucleus were not shortened. To identify enzymes responsible for nuclear deadenylation, RNA targeting Cas-systems, siRNAs and shRNA cell lines were used to different deadenylase complexes. Despite efficient mRNA knockdown, subcellular analysis of poly(A) tail length by did not yield molecular phenotypes of changing nuclear poly(A) tail length.