Metagene profiling

Note

Be aware that we are still actively working on our metagene profiling scripts. The main functionality is available, but the plotting scripts may be subject to further changes. Result tables in .tsv and .xlsx format are provided if you prefer using your own plotting scripts.

In this section, we describe the metagene profiling data provided by HRIBO.

What is metagene profiling?

Metagene profiling analyses the distribution of mapped reads around annotated start codons and stop codons.

For Ribo-seq it is expected that the ribosome protects a specific range of read lengths, often typical for the investigated group of organisms, from digestion by nuclease. These reads should show a typical peak around the start codon which corresponds to the high frequency that ribosomes are bound there.

We output and plot the metagene profiling for each individual fragment length as a quality control for the Ribo-seq protocol. If the distribution for all read lengths is untypical, arresting the ribosomes failed.

Metagene output

We provide the possibility to analyse the metagene profiling for different coverage mapping methods. For each sample, both the start and stop codon profiling will be performed and plotted.

Furthermore, we provide normalized (cpm, window) and unnormalized (raw) version for each sample analysed. The normalized version takes into account the total value for each position, as well as the current window length.

The metagene profiling is done for each sample and each coverage mapping method.

• global: full read is mapped

• centered: region around the center is mapped

• threeprime: region around the three prime end is mapped

• fiveprime: region around the five prime end is mapped

For each of these sample/mapping combinations, result tables in excel .xlsx format are provided, as well as an interactive plot or several static plots.

Note

New filtering methods were added. For details check the options section.

Example output

Note

This section contains old output figures, but these are still valid as the overall method did not change.

In the following, we show a few example figure of how the results can look like.

This example shows a very sharp peak around +17nt. (mapping: threeprime)

30	31	32	33	34	sum	coordinates
…	…	…	…	…	…	…
1321	1242	1269	773	92	4697	11
1564	1193	1358	624	172	4911	12
4272	2317	1659	906	125	9279	13
6870	3009	1814	738	91	12522	14
6214	7498	2733	1265	140	17850	15
17797	27399	28398	13322	1245	88161	16
7686	22687	62042	25575	5427	123417	17
1272	5200	11156	11202	1382	30212	18
1584	3520	10027	22530	5325	42986	19
792	2166	2636	5044	1969	12607	20
176	568	1082	931	307	3064	21
233	321	930	1102	219	2805	22
204	211	270	378	89	1152	23
…	…	…	…	…	…	…

The tables give a better overview over the data, showing that the highest peak is found at +17 for a read length of 32nt.

In this example the peak is less sharp, but it is still clearly distinguishable and around +13nt. (mapping: threeprime)

26	27	28	29	30	sum	coordinates
…	…	…	…	…	…	…
294	308	198	297	321	1418	9
177	338	204	170	286	1175	10
217	236	248	164	162	1027	11
2545	804	976	1379	548	6252	12
8593	10334	5286	4359	5519	34091	13
6120	7662	3238	2403	3590	23013	14
3416	4134	3807	2633	1643	15633	15
1311	4097	5079	5528	3020	19035	16
270	340	1086	1682	1222	4600	17
202	282	269	632	761	2146	18
…	…	…	…	…	…	…

This table shows a situation that is less clear, the offset at +13 for read length 27nt is the highest, while it is very close to the surounding values.

The metagene analysis is very dependant on the analysed data. We have observed cases with multiple peaks, with one large peak and a few slightly smaller peaks.

This shows a case with a strong peak at around -15nt, while there are multiple smaller peaks at -22, +112, +219, and +291. These kinds of peaks can have multiple reasons. Reasons we observed so far were:

• tRNA or rRNA that was not filtered out correctly

• singular genes that have a large number of reads attributed to them (due to a biological reason or a faulty sequencing)