Hi Agusman Agusman, 

This is normal and will all depend on the pipeline that was used to classify your sequences. Commonly QIIME or mothur is used, and taxonomy is assigned by comparing your sequences to a reference database. Databases include UNITE, RDP, ITSone and many others. The problem with these databases is that they are over represented with pathogenic fungi (ofter human or animal pathogens), fungi of biotechnological uses or mycorrhizal fungi. As a result many of the common soil/aquatic fungi are omitted, and sequences that would have been assigned to these taxonomic identities remain as unclassified in your dataset.  

Can you please give more information on how the data was analysed? Then I can give you better feedback on what you can do. Do you expect a lot of fungi to be novel to your system? 

Something I have done to bypass this was to use references to the most complete database at the time (RDP), or use multiple databses. You can find what I have done in the methods section of the paper referenced below.

Hope this helps.

Andries

Article Unique Microbial Phylotypes in Namib Desert Dune and Gravel ...

Dear Andreas

Thank you so much for replay. Actually I want to study the community of fungi associated with marine algal and subtract in which algal attach. Since, I haven’t experience on fungi and illumine sequencing， I decided sent the sample into company and help me to analyses the result. I read some article studying the marine fungi which isolated from marine sediment I found that they results only contain small amount of Fungi Unidentified.

The raw sequencing of Illumina PE was analyzed using the Trimmatic , Flash. According to the overlap relationship between PE reads, pairs will be merged into a sequence, the minimum overlap length of 10bp;The maximum mismatch ratio allowed for the overlap region of the spliced sequence is 0.2, and the filter does not conform to the sequence; According to the barcode and primers at both ends of the sequence, the samples were aligned and the sequence direction was adjusted. The bar code allowed the mismatch number to be 0 and the maximum number of mismatches was 2.

The average of trimming read sequencing are about 245.61 bp (input 250 bp). The sequences are groups using Usearch (Uparse). Alpa diversity was analyzed using mother. UNITE, RDP were used as references database. For reference database. Qiime and RDP classifier were used for taxonomic

Thank so much for advice and the article

Regard Agus

Dear Agusman. Its a normal result when you determine diversity of microorganisms with total DNA (Metagenome) as template, you will get about 80 % as unclassified or uncultured.  99% of microorganisms nature  are uncultured but the DNA can be analyzed using ilumina sequencer. I am working with ilumina too now. But  I am not familiar too with data analyzing. 

good luck

Dewi S, Zilda